Error Threshold Research Articles

Comprehensive Analyses on the Influential Factors in Supersonic Combustion Simulation Using Dynamic Adaptive Chemistry Method

ABSTRACT To overcome the major challenge of reactive flow simulation for chemical kinetics dominated flame dynamics in supersonic combustion, on-the-fly mechanism reduction for high fidelity simulation of scramjet becomes mandatory. For dynamic adaptive chemistry (DAC) methodology, there are three major factors controlling the accuracy and efficiency of the overall simulation, namely, the mechanism reduction method, error threshold value ε DAC , and search initiating species (SIS). In the present work, systematic investigations of the three influential factors were conducted for large eddy simulation of ethylene-fueled supersonic combustion within a unified DAC framework. The results show that all the four mechanism reduction methods, i.e., DRG, DRGEP, PFA, and DAC-L, are adequate for the combustor’s global performance prediction regarding the wall pressure, stable combustion productions, and temperature. However, for intricate flame stabilization characteristics, the DRG, DRGEP, and DAC-L methods yield comparable prediction accuracy in radical distributions, whereas the PFA method leads to relatively large discrepancies compared to direct integration with the detailed mechanism. The DRGEP method obtains the best balance between numerical accuracy and computational efficiency among the four methods, while the PFA method is the most computationally demanding one. Regarding the mechanism reduction error threshold value, the relative errors in physical property predictions increase as the relaxation of the error threshold value. And the comparative study suggests that the ε DAC should not exceed 10 − 4 for high fidelity simulations of supersonic combustion. Furthermore, the stable species combination, namely, fuel, O2, and N2 incurs larger relative errors in radical mass fraction prediction than the combination including fuel and intermediate species HO2 and CO. Nevertheless, the latter is less computationally efficient than the former as it requires 15% more CPU time to solve the stiff ODE system of the resultant skeletal mechanism. It should be noted that the computational overheads for mechanism reduction under various ε DAC values and SIS combinations are almost the same, and the overall computational efficiency is mainly determined by the CPU time for solving the stiff ODE system of the size-reduced skeletal mechanisms.

Off‐grid direction‐of‐arrival estimation for wideband noncircular sources

AbstractResearchers have recently shown an increased interest in estimating the direction‐of‐arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace‐based methods. An off‐grid sparse recovery‐based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal‐to‐noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer–Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results.

Enlargement of the Pancreas in Children Diagnosed with Acute Pancreatitis: An Approach Based on P/V Ratio.

Background Acute pancreatitis is a reversible inflammation of the pancreatic parenchyma. Enlargement of the pancreas is often envisaged in such conditions. This study evaluates P/V ratio, as a marker to decide pancreatic enlargement in disease condition. Purpose The aim of this study was to develop imaging-based diagnostic criterion for acute pancreatitis in children based on P/V ratio. Material and Methods This study included 37 children with acute pancreatitis and 283 children asymptomatic for pancreatic disorders, from a single hospital center. The age of children ranged between 2 and 18 years. P/V ratio, which is the ratio of greatest anteroposterior dimension of the head, body, and tail of the pancreas relative to the transverse lumbar vertebral body, was obtained for each child through ultrasonographic examination. Age-adjusted receiver operating characteristics (AROC) analysis was performed on P/V ratio at presentation for each pancreatic region, and the sensitivity at 90% specificity, the threshold errors, and the corresponding cutoffs were obtained. The enlargement assessment was also done after clinical recovery by referring to the cutoffs of respective regions. Results AROC analysis for males and females resulted into a maximum sensitivity of 83.33 and 81.67%, respectively, at 90% specificity for head. The error thresholds for both the groups were same, i.e., 0.098, indicating that 90% of the observations had errors less than the threshold. The corresponding P/V ratio cutoff for males and females was 0.43 and 0.42, respectively. Conclusion Radiologists and clinicians can refer a cutoff value of 0.4 for each region, along with hypoechogenicity, to decide about enlargement of the pancreas in acute pancreatitis condition.

Deconfinement and error thresholds in holography

We study the error threshold properties of holographic quantum error-correcting codes. We demonstrate that holographic CFTs admit an algebraic threshold, which is related to the confinement-deconfinement phase transition. We then apply geometric intuition from holography and the Hawking-Page phase transition to motivate the CFT result, and comment on potential extensions to other confining theories.

Evaluation of simple and multivariate linear regression models for exchangeable base cation conversion between seven measurement techniques on South African soils

Conversions of exchangeable base cation concentrations between different analytical methods are required for comparisons of soil test results with norm values developed using specific methods. Different methods vary in extraction efficiency due to underlying chemical mechanisms of cation exchange. There is a lack of knowledge pertaining to the combined effects of soil physicochemical properties on the accuracy and parsimony of conversion models. Thus, the aim of this study was to develop equations for conversion of exchangeable base cation (Ca, Mg, K and Na) concentrations between seven different methods of measurement for soils dominated by moderate-activity clays. Additional objectives included the discussion of chemical mechanisms influencing nutrient extraction efficiency, development of root mean square error (RMSE) thresholds for model selection, and evaluation of the effects of soil physicochemical properties on model performance. The ammonium acetate (1 M and 0.2 M, pH = 7), compulsive exchange, Ambic-I, Mehlich-III, Bray-II and Citric acid (1%) soil test methods were performed on 57 soils from a Mediterranean climatic zone. Simple linear regressions (SLR) as well as multivariate linear regressions (MLR) incorporating soil physicochemical properties as parameters were developed. The accuracy of cross-validated models was comparable to accuracy of predictions for samples included in calibration. MLR equations (RMSECV = 1.4–1.6 cmolc kg−1, 0.4 cmolc kg−1, 16–20 mg kg−1 and 25–40 mg kg−1 for Ca, Mg, Na and K, respectively) were generally more accurate than SLR models. Relationships between Ca content extracted by different methods depended on soil alkalinity. Use of the developed SLR or MLR models to convert nutrient quantities between different methods should be chosen based on contextual accuracy requirements.

Quantum Key Distribution: Modeling and Simulation through BB84 Protocol Using Python3.

Autonomous “Things” is becoming the future trend as the role, and responsibility of IoT keep diversifying. Its applicability and deployment need to re-stand technological advancement. The versatile security interaction between IoTs in human-to-machine and machine-to-machine must also endure mathematical and computational cryptographic attack intricacies. Quantum cryptography uses the laws of quantum mechanics to generate a secure key by manipulating light properties for secure end-to-end communication. We present a proof-of-principle via a communication architecture model and implementation to simulate these laws of nature. The model relies on the BB84 quantum key distribution (QKD) protocol with two scenarios, without and with the presence of an eavesdropper via the interception-resend attack model from a theoretical, methodological, and practical perspective. The proposed simulation initiates communication over a quantum channel for polarized photon transmission after a pre-agreed configuration over a Classic Channel with parameters. Simulation implementation results confirm that the presence of an eavesdropper is detectable during key generation due to Heisenberg’s uncertainty and no-cloning principles. An eavesdropper has a 0.5 probability of guessing transmission qubit and 0.25 for the polarization state. During simulation re-iterations, a base-mismatch process discarded about 50 percent of the total initial key bits with an Error threshold of 0.11 percent.

Invalid point removal method based on error energy function in fringe projection profilometry

Invalid points, such as shadow and background, in the captured fringe patterns of fringe projection profilometry (FPP) are often inevitable due to the limited field of view measurements of three-dimensional (3D) imaging equipment. To ensure the quality of 3D reconstruction data, these invalid points must be identified and removed. FPP captures co-frequency-based fringe pattern sequences and approximately distributes this data along an ideal cosine curve. We propose an invalid points removal method based on an error energy function. By analyzing the relationship between the pixel values of a series of captured co-frequency patterns and an ideal cosine curve, we quantize the error energy by using a Gaussian weighted Euclidean distance. An improved Gaussian filtering method based on modulation intensity is used to significantly increase the error energy difference between the valid points of the target and the invalid points of the shadow/background area. Finally, the points whose error energy is greater than a certain error threshold are removed as invalid points. Experimental results indicate that this method can efficiently remove invalid points in fringe patterns and performs better than existing traditional methods.

Realizing coherently convertible dual-type qubits with the same ion species

Trapped ions constitute one of the most promising systems for implementing quantum computing and networking1,2. For large-scale ion-trap-based quantum computers and networks, it is critical to have two types of qubit: one for computation and storage, and another for auxiliary operations such as qubit detection3, sympathetic cooling4–7 and entanglement generation through photon links8,9. Although the two qubit types can be implemented using two different ion species3,10–13, this approach introduces substantial complexity into creating and controlling each qubit type14,15. Here we resolve these challenges by implementing two coherently convertible qubit types using one ion species. We encode the qubits into two pairs of clock states of the 171Yb+ ions, and achieve microsecond-level conversion rates between the two types with one-way fidelities of 99.5%. We further demonstrate that operations on one qubit type, including sympathetic laser cooling, single-qubit gates and qubit detection, have crosstalk errors less than 0.06% on the other type, which is below the best-known error threshold of ~1% for fault-tolerant quantum computing using the surface code1,16. Our work establishes the feasibility and advantages of using coherently convertible dual-type qubits with the same ion species for large-scale quantum computing and networking. Quantum computing with trapped ions requires qubits that can store and manipulate quantum information, and others that can be used for destructive incoherent operations. Different states of ytterbium-171 ions can be used to realize both qubit types

Wireless Sensor Modeling Optimization Algorithm Based on Artificial Intelligence Neural Network

With the development and progress of society, science and technology have entered the field of view of scholars at home and abroad. As the science and technology with the biggest potential in recent years, wireless sensor network has been involved in many scientific fields. This paper aims to study the wireless sensor modeling optimization algorithm of artificial intelligence neural network. In this paper, a WSN data fusion algorithm (DFRMP) based on regression model prediction is proposed, and the four algorithms of SLR, PAQ, TINA, and DFRMP are compared. The experimental results of this paper show that when the mean square error and mean absolute error are not much different, the data transmission rate of DFRMP algorithm is the smallest. When the absolute error threshold is 1, the data transfer rates of these four algorithms are 0.033, 0.0327, 0.035, and 0.017, respectively. This shows that the DFRMP algorithm proposed in this paper has superior performance.

Forensic Dental Age Estimation of North Indian Children Using Three Radiological Scoring Methods

Although believed to be reliable in children, dental age estimations have reportedly shown variations in their accuracy levels, indicating regional differences. The present paper aims to study the error threshold of the Nolla, Demirjian and Willems methods for age estimation of North Indian children. Digital orthopantomograms of 168 children aged 3-15 years were analyzed for the three methods. Demirjian dental age was found to be closest to the mean chronological age, as indicated by the p-value of paired t-test. The error range for the Willems and Demirijian methods was ±5 years with 87.5% of cases showing error within the range of ±2 years, and it was ±6 years with 84.5% of cases showing error within ±2 years using the Nolla method. The mean absolute error for the Nolla, Demirjian and Willems methods was 1.09, 1.10 and 0.97 years, respectively. The Willems method was better suited for dental age estimation of the studied population, though none of the methods precisely estimated the age of the participants. Wide variations in age estimates of varied population groups may be due to differences in statistical methods or genetic factors. Thus, comparison of the methods for different populations by the same researcher is suggested to reduce certain biasness (statistical methodology) in the study.

Credit Card Fraud Detection using Autoencoders

In today’s life or economy credit card plays a veryimportant role. Credit card becomes a necessary part of business, household and bank transactions. Using of credit card carefully and responsibly gives a enormous benefit to the user, fraudulent activities happen and give financial damage to the user or card holder. The growth of E-commerce industry led to use of credit card or many platform for online purchase or different transaction because of this the fraudulent activities was also increased. Bank facing many issues for detecting the credit card fraudulent transactions. For finding the fraud of credit card machine learning plays a vital role. For predicting these fraud detection of credit card we use many machine learning methods or algorithms, past data we collect and analyze it and make a machine learning model to detect the fraudulent activities which going to happen. The performance of fraud detecting in credit card transaction is greatly affected by sampling approach on data-set, selection of variable and detection technique used. This project objective to use of efficient approach to detect automatic fraud related to banks or insurance company using deep learning algorithm called autoencoder. We are using the European card holder real-time dataset of September 2013. The data was unbalanced in the dataset so for this autoencoder is perfect to provide the accurate results. We can reconstruct the normal data through the autoencoder and anomalies was detected at the time of reconstruction error threshold and consider the anomalies. Keyword: fraud detection, unsupervised learning, autoencoder, credit card

Measurement-based logical qubit entanglement purification

Entanglement purification is the distilling of high-quality entanglement from low-quality entanglement and is a key element in the quantum repeater. As a new branch of entanglement purification, the measurement-based entanglement purification protocol (MBEPP) only requires one to perform the Bell state measurement to couple resource states with noisy pairs, and it tolerates more local noise than the conventional purification protocols. Existing MBEPPs usually focus on physical qubit entanglement. In this paper, we propose a measurement-based logical qubit entanglement purification protocol (MBLEPP) with quantum nondemolition detection (QND), where the qubit is encoded in the quantum parity code. The results show that this MBLEPP can also work with photon loss under the conditions that each block of the logical Bell state measurement contains at least one physical qubit, that at least one of the blocks is intact, and that the entanglement exists between all blocks. Moreover, we also consider the MBLEPP with imperfect QND. We show that below a certain QND error threshold, this MBLEPP can still work. In this way, this MBLEPP not only obtains high-fidelity entanglement but also tolerates photon loss and the error from imperfect QND. This MBLEPP combines the benefits of the MBEPP and quantum error correction code and may have potential application in long-distance quantum communication.

Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder

Model-based methods can be used to detect anomalies in industrial robots, but they require a high level of expertise and are therefore difficult to implement. The lack of sufficient data on the anomalous operation of industrial robots limits data-driven anomaly detection methods. This study proposes Sliding Window One-Dimensional Convolutional Autoencoder (SW1DCAE), an unsupervised vibration anomaly detection algorithm for industrial robots, that can directly act on the original vibration signal and effectively improve detection accuracy. First, the convolutional neural network and the autoencoder model are effectively integrated to construct a one-dimensional convolutional autoencoder model. Secondly, the sliding window algorithm is used for data enhancement, and the dropout technique is introduced to improve the generalization ability of the model. Finally, the reconstruction error of the input sample is calculated and compared with the error threshold to determine whether the operation state of the industrial robot is normal or not. This study discusses the effect of different convolution kernel widths, sliding window sizes, dropout ratios, and other parameters on model performance. Validation with vibration signals collected from an industrial robot test bench shows that this unsupervised anomaly detection algorithm has good accuracy and F1 score.

Traditional Ceramic Sculpture Feature Recognition Based on the Machine Learning Algorithm

Aiming at the problems of low recognition accuracy, high line noise, and high time cost of feature recognition in traditional ceramic sculpture modeling feature recognition methods, this paper designed a traditional ceramic sculpture modeling feature recognition method based on the machine learning algorithm. By constructing the sparse representation model of a traditional ceramic sculpture image, the posterior probability of a traditional ceramic sculpture image was determined and Gaussian distribution of pixels in the image was carried out to determine the distribution law of pixels in the image and the super-resolution reconstruction of the traditional ceramic sculpture image was realized. The feature of the line state of a traditional ceramic sculpture image was determined and classified by the kernel classification method. Finally, the machine learning algorithm red BP neural network is introduced to construct the traditional ceramic sculpture modeling feature recognition algorithm and the error threshold is constantly iterated to realize the traditional ceramic sculpture modeling feature recognition. The experimental results show that the recognition algorithm designed in this paper has high accuracy in identifying the traditional ceramic sculpture features, can effectively suppress the line noise, and has a short recognition time overhead, which has a certain feasibility.

Visual Reassessment with Flux-Interval Plot Configuration after Automatic Classification for Accurate Atrial Fibrillation Detection by Photoplethysmography.

Atrial fibrillation (AFib) is a common type of arrhythmia that is often clinically asymptomatic, which increases the risk of stroke significantly but can be prevented with anticoagulation. The photoplethysmogram (PPG) has recently attracted a lot of attention as a surrogate for electrocardiography (ECG) on atrial fibrillation (AFib) detection, with its out-of-hospital usability for rapid screening or long-term monitoring. Previous studies on AFib detection via PPG signals have achieved good results, but were short of intuitive criteria like ECG p-wave absence or not, especially while using interval randomness to detect AFib suffering from conjunction with premature contractions (PAC/PVC). In this study, we newly developed a PPG flux (pulse amplitude) and interval plots-based methodology, simply comprising an irregularity index threshold of 20 and regression error threshold of 0.06 for the precise automatic detection of AFib. The proposed method with automated detection on AFib shows a combined sensitivity, specificity, accuracy, and precision of 1, 0.995, 0.995, and 0.952 across the 460 samples. Furthermore, the flux-interval plot configuration also acts as a very intuitive tool for visual reassessment to confirm the automatic detection of AFib by its distinctive plot pattern compared to other cardiac rhythms. The study demonstrated that exclusive 2 false-positive cases could be corrected after the reassessment. With the methodology’s background theory well established, the detection process automated and visualized, and the PPG sensors already extensively used, this technology is very user-friendly and convincing for promoted to in-house AFib diagnostics.

Absolute convergence and error thresholds in non-active adaptive sampling

Non-active adaptive sampling is a way of building machine learning models from a training data base which are supposed to dynamically and automatically derive guaranteed sample size. In this context and regardless of the strategy used in both scheduling and generating of weak predictors, a proposal for calculating absolute convergence and error thresholds is described. We not only make it possible to establish when the quality of the model no longer increases, but also supplies a proximity condition to estimate in absolute terms how close it is to achieving such a goal, thus supporting decision making for fine-tuning learning parameters in model selection. The technique proves its correctness and completeness with respect to our working hypotheses, in addition to strengthening the robustness of the sampling scheme. Tests meet our expectations and illustrate the proposal in the domain of natural language processing, taking the generation of part-of-speech taggers as case study.

A Novel Two-Stage Deep Learning Structure for Network Flow Anomaly Detection

Unknown cyber-attacks have appeared constantly. Several anomaly detection techniques based on semi-supervised learning have been proposed to detect these unknown cyber-attacks. Among them, the Denoising Auto-Encoder (DAE) scheme performs better than others in accuracy but is not good enough in precision. This paper proposes a novel two-stage deep learning structure for network flow anomaly detection by combining the models of Gate Recurrent Unit (GRU) and DAE. By using supervised anomaly detection with a selection mechanism to assist semi-supervised anomaly detection, the precision and accuracy of the anomaly detection system are improved. In the proposed structure, we first use the GRU model to analyze the network flow and then take the outcome from the Softmax function as a confidence score. When the score is more than or equal to the predefined confidence threshold, the GRU model outputs the flow as a positive result, no matter the flow is classified as normal or abnormal. When the score is less than the confidence threshold, GRU model outputs the flow as a negative result and passes the flow to DAE model for flow classification. DAE then determines a reconstruction error threshold by learning the pattern of normal flows. Accordingly, the flow is normal or abnormal depending on whether it is under or over the reconstruction error threshold. A comparative experiment is performed using NSL-KDD dataset as benchmark. The results revealed that the precision using the proposed scheme is 0.83% better than DAE. The accuracy using the proposed approach is 90.21%, which is better than Random Forest, Naïve Bayes, One-Dimensional Convolutional Neural Network, two-stage Auto-Encoder, etc. In addition, the proposed approach is also applied to the environment of software defined network (SDN). By adopting our approach in SDN environment, the precision and F-measure are significantly improved.

Predicting non-uniform indoor air quality distribution by using pulsating air supply and SVM model

Mixing ventilation is the most common air distribution strategy, and often the same diffusers provide space cooling and heating. Although aiming to achieve uniform air distributions, there are still non-uniformities at specific indoor locations. This study innovatively predicts the non-uniform indoor air quality (IAQ) distribution with mixing ventilation, directly from air temperature and air velocity. Both heating and cooling cases are conducted with computational fluid dynamics (CFD) techniques, validated by the experimental measurements in a multi-occupant office configuration. The comparison shows that the support vector machine (SVM) model outperforms the back-propagation neural network (BPNN) and genetic algorithm back-propagation neural network (GABPNN) models, with a medium computation time. The innovative method is demonstrated that the pulsating air supply is useful in generating effective inputs, i.e., the dynamic variations of air temperature and velocity during pulsating air supply (ΔT and Δv), for the air age prediction under the corresponding steady state (τsteady). ΔT and Δv can be fast obtained by monitoring the air velocity and temperature for 10–20 min. With a random selection, the data size should be larger than 180 to reach the mean absolute percentage error (MAPE) threshold value of 5%. However, it can also be reduced to 60 when the data points have a similar steady air temperature. The prediction accuracy under heating is slightly higher than that under cooling, as achieving good mixing under heating is more difficult. We aim to provide guidelines on effective and measurable inputs and valid data-driven models for non-uniform IAQ prediction.

Anomaly detection in rotating machinery using autoencoders based on bidirectional LSTM and GRU neural networks

A time series anomaly is a form of anomalous subsequence that indicates future faults will occur. The development of novel techniques for detecting this type of anomaly is significant for real-time system monitoring. Several algorithms have been used to classify anomalies successfully. However, the time series anomaly detection algorithm was not studied well. We use a new bidirectional LSTM and GRU neural networks-based hybrid autoencoder to detect if a machine is operating normally in this research. An autoencoder is trained on a set of 12 features taken from healthy operating data gathered promptly after a planned maintenance period using vibration sensors. The features taken from new data are then reconstructed using the trained model. If the model accurately reconstructs the features, the machine is in good working order. If the reconstruction exceeds a certain error threshold, the machine is functioning strangely and needs to be serviced.

Flexible selection framework for secondary frequency regulation units based on learning optimisation method

Grid connections of new energy sources have increased system frequency fluctuations, which requires secondary frequency regulation(SFR) to ensure the safe and stable operation of the system. SFR balances the power deviation between generation and consumption in real time by changing the setpoints of the units. In this paper, we propose a dynamic area control error (ACE) threshold value-based method to rationalise unit selection for SFR tasks. Each conventional unit or virtual generation unit has an independent threshold to aid dispatchers in determining the response priority of each unit to SFR tasks relative to scheduled tasks. In this manner, power generation units can switch their control mode flexibly and track different setpoints using automatic generation control(AGC) and load control technology. We designed a threshold solver based on a sequential decision framework using the deep reinforcement learning method. The solver can dynamically update thresholds based on the real-time system operational state such that the selected units can better meet the current dispatching needs. In the experiment, we tested the performance of the solver in several typical scenarios; the proposed method was found to be effective in maintaining the frequency stability of the system, with lower operation costs.