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.

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.

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.

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.

Investigation of Error Detection Capabilities of Various Patient-Specific Intensity Modulated Radiotherapy Quality Assurance Devices

The capability of error detection of patient-specific QA tools plays an important role in verifying MLC motion accuracy. The goal of this study was to investigate the capability in error detection of portal dosimetry, MapCHECK2 and MatriXX QA tools in IMRT plans. The 9 fields IMRT for 4 head and neck plans and 7 fields IMRT for 4 prostate plans were selected for the error detection of QA devices. The measurements were undertaken for the original plan and the modified plans, where the known errors were introduced for increasing and decreasing of prescribed dose (±2%, ±4% and ±6%) and position shifted in X-axis and Y-axis (±1, ±2, ±3 and ±5 mm). After measurement, the results were compared between calculated and measured values using gamma analysis at 3%/3 mm criteria. The average gamma pass for no errors introduced in head and neck plans was 96.9%, 98.6%, and 98.8%, while prostate plans presented 99.4%, 99.0%, and 99.7%, for portal dosimetry, MapCHECK2 and MatriXX system, respectively. In head and neck plan, the shifted error detections were 1 mm for portal dosimetry, 2 mm for MapCHECK2, and 3 mm for MatriXX system. In prostate plan, the shifted error detections were 2 mm for portal dosimetry, 3 mm for MapCHECK2, and 5 mm for MatriXX system. For the dose error detection, the portal dosimetry system could detect at 2% dose deviation in head and neck and 4% in prostate plans, while other two devices could detect at 4% dose deviation in both head and neck and prostate plans. Portal dosimetry shows slightly more capability to detect the error compared with MapCHECK2 and MatriXX system, especially in the complicated plan. It may be due to higher resolution of the detector;however, all three-detector types can detect various errors and can be used for patient-specific IMRT QA.

Error-detected single-photon quantum routing using a quantum dot and a double-sided microcavity system

Based on a hybrid system consisting of a quantum dot coupled with a double-sided micropillar cavity, we investigate the implementation of an error-detected photonic quantum routing controlled by the other photon. The computational errors from unexpected experimental imperfections are heralded by single photon detections, resulting in a unit fidelity for the present scheme, so that this scheme is intrinsically robust. We discuss the performance of the scheme with currently achievable experimental parameters. Our results show that the present scheme is efficient. Furthermore, our scheme could provide a promising building block for quantum networks and distributed quantum information processing in the future.

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.

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.

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.

SEDSR: Soft Error Detection Using Software Redundancy

This paper presents a new method for soft error detection using software redundancy (SEDSR) that is able to detect transient faults. Soft errors damage the control flow and data of programs and designers usually use hardware-based solutions to handle them. Software-based techniques for soft error detection force less cost and delay to systems and do not change their configuration. Therefore, these kinds of methods are appropriate alternatives for hardware-based techniques. SEDSR has two separate parts for data and control flow errors detection. Fault injection method is used to compare SEDSR with previous methods of this field based on the new parameter of “Evaluation Factor” that takes in account fault coverage, memory and performance overheads. These parameters are important in real time safety critical applications. Experimental results on SPEC2000 and some traditional benchmarks of this field show that SEDSR is much better than previous methods of this field. SEDSR’s evaluation factor is about 50% better than other methods of this field. These results show its success in satisfaction of the existing tradeoff between fault coverage, performance and memory overheads.

ERROR-DETECTING CODES AND UNDETECTED ERROR PROBABILITIES

The definition of good codes for error-detection is given. It is proved that a (n, k) linear block code in GF(q) are the good code for error-detection, if and only if its dual code is also. A series of new results about the good codes for error-detection are derived. New lower bounds for undetected error probabilities are obtained, which are relative to n and k only, and not the weight structure of the codes.

Sensitivity and Specificity Analysis Relation to Statistical Hypothesis Testing and Its Errors: Application to Cryptosporidium Detection Techniques

The use of Statistical Hypothesis Testing procedure to determine type I and type II errors was linked to the measurement of sensitivity and specificity in clinical trial test and experimental pathogen detection techniques. A theoretical analysis of establishing these types of errors was made and compared to determination of False Positive, False Negative, True Positive and True Negative. Experimental laboratory detection methods used to detect Cryptosporidium spp. were used to highlight the relationship between hypothesis testing, sensitivity, specificity and predicted values. The study finds that, sensitivity and specificity for the two laboratory methods used for Cryptosporidium detection were low hence lowering the probability of detecting a “false null hypothesis” for the presence of cryptosporidium in the water samples using either Microscopic or PCR. Nevertheless, both procedures for cryptosporidium detection had higher “true negatives” increasing its probability of failing to reject a “true null hypothesis” with specificity of 1.00 for both Microscopic and PCR laboratory detection methods.

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.

The co-phasing detection method for sparse optical synthetic aperture systems

Co-phasing between different sub-apertures is important for sparse optical synthetic aperture telescope systems to achieve high-resolution imaging. For co-phasing detection in such a system, a new aspect of the system＇s far-field interferometry is analysed and used to construct a novel method to detect piston errors. An optical setup is built to demonstrate the efficacy of this method. Experimental results show that the relative differences between measurements by this method and the criterion are less than 4%, and their residual detecting errors are about 0.01 A for different piston errors, which makes the use of co-phasing detection within such a system promising.

Detecting Circles Using a Two-Stage Approach

We propose a two-stage method for detecting circular objects in this paper. In the first stage, curves are divided as linear segments or nonlinear segments. A least square estimator is used to find the estimated centers and radii of the nonlinear segments in the second stage. The found centers and radii are then evaluated to see if there exist circles in the nonlinear segments. Both of the broken and occluded circular objects are evaluated for the proposed method. From the experimental results, it is seen that the proposed method is efficient.

Low-Complexity Signal Detection Based on Relaxation Iteration Method in Massive MIMO Systems

Minimum mean square error(MMSE) detection algorithm can achieve nearly optimal performance when the number of antennas at the base station(BS) is large enough compared to the number of users. But the traditional MMSE involves complicated matrix inversion. In this paper, we propose a modified MMSE algorithm which exploits the channel characteristics occurring in massive multiple-input multipleoutput(MIMO) channels and the relaxation iteration(RI) method to avoid the matrix inversion. A proper initial solution is given to accelerate the convergence speed. In addition, we point out that the channel estimation scheme used in channel hardening-exploiting message passing(CHEMP) receiver is very appropriate for our proposed detection algorithm. Simulation results verify that the proposed algorithm can achieve very close performance of the traditional MMSE algorithm with a small number of iterations.

M-ary information processing via an optimal nonlinear detector

This paper presents a novel approach of M-ary baseband pulse amplitude modulated signal processing via a parameter-optimized nonlinear dynamic system. This nonlinear system usually shows the phenomenon of stochastic resonance by adding noise. To thoroughly discuss the signal processing performance of the nonlinear system, we tune the system parameters to obtain a nonlinear detector with optimal performance. For characterizing the output of the nonlinear system, the derivation of the probability of detection error is given by the system response speed and the probability density function of the nonlinear system output. By varying the noise intensity with fixed system parameters, the phenomenon of stochastic resonance is shown and by tuning the system parameters with fixed noise, the probability of detection error is minimized and the nonlinear system is optimized. The detection performance of the two cases is compared with the theoretical probability of detection error, which is validated by numerical simulation.