Application of Radial Basis Function Network in Sensor Failure Detection

Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.

Anomaly Detection Using Data Rate of Change on Medical Data

The identification and mitigation of anomaly data,characterized by deviations from normal patterns or singularities,stand as critical endeavors in modern technological landscapes,spanning domains such as Non-Fungible Tokens(NFTs),cyber-security,and the burgeoning metaverse.This paper presents a novel proposal aimed at refining anomaly detection methodologies,with a particular focus on continuous data streams.The essence of the proposed approach lies in analyzing the rate of change within such data streams,leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy.Through empirical evaluation,our method demonstrates a marked improvement over existing techniques,showcasing more nuanced and sophisticated result values.Moreover,we envision a trajectory of continuous research and development,wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios,ensuring adaptability and robustness in real-world applications.

Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcrafi Unmanned Aerial Vehicle （RUAV） sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.

An Efficient Adaptive Failure Detection Mechanism for Cloud Platform Based on Volterra Series

Failure detection module is one of important components in fault-tolerant distributed systems,especially cloud platform.However,to achieve fast and accurate detection of failure becomes more and more difficult especially when network and other resources' status keep changing.This study presented an efficient adaptive failure detection mechanism based on volterra series,which can use a small amount of data for predicting.The mechanism uses a volterra filter for time series prediction and a decision tree for decision making.Major contributions are applying volterra filter in cloud failure prediction,and introducing a user factor for different QoS requirements in different modules and levels of IaaS.Detailed implementation is proposed,and an evaluation is performed in Beijing and Guangzhou experiment environment.

SENSOR FAILURE DETECTION AND SIGNAL RECOVERY BASED ON BP NEURAL NETWORK

A study is given on the application of BP neural network (BPNN) in sensorfailure detection in control systems, and on the networ architecture desgn, the redun-dancy,the quickness and the insensitivity to sensor noise of the BPNN based sensor detec-tion methed. Besules, an exploration is made into tbe factors accounting for the quality ofsignal recovery for failed sensor using BPNN. The results reveal clearly that BPNN can besuccessfully used in sensor failure detection and data recovery.

Automatic System for Failure Detection in Hydro-Power Generators

This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interference in the generator operation. The detected failures could be mechanical or electrical origins, such as: problems in bearings, unwanted vibrations, partial discharges, misalignment, unbalancing, among others. It is possible because the generator acts as a transducer for mechanical problems, and they appear in current and voltage signals. This automatic system based on electric signature analysis has been installed in Itapebi Power Plant generators since 2012. Some results are presented in this paper.

Predicting heartbeat arrival time for failure detection over internet using auto-regressive exogenous model

Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model＇ s coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous （ARX） model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

Adenoma and advanced neoplasia detection rates increase from 45 years of age

BACKGROUND Colonoscopy is considered a valid primary screening tool for colorectal cancer(CRC). The decreasing risk of CRC observed in patients undergoing colonoscopy is correlated with the adenoma detection rate(ADR). Due to the fact that screening programs usually start from the age of 50, very few data are available on the risk of adenoma between 40 and 49 years. However, the incidence of CRC is increasing in young populations and it is not uncommon in routine practice to detect adenomas or even advanced neoplasia during colonoscopy in patients under 50 years.AIM To compare the ADR and advanced neoplasia detection rate(ANDR) according to age in a large series of patients during routine colonoscopy.METHODS All consecutive patients who were scheduled for colonoscopy were included.Exclusion criteria were as follows: patients scheduled for partial colonoscopy or interventional colonoscopy(for stent insertion or stenosis dilation).Colonoscopies were performed in our unit by a team of 30 gastroenterologists in2016. We determined the ADR and ANDR in each age group in the whole population and in the population with an average risk of CRC(excluding patients with personal or family history of advanced adenoma or cancer).RESULTS6027 colonoscopies were performed in patients with a median age of 57 years(range, 15-96). The ADR and ANDR were 28.6% and 9.7%, respectively, in the whole population. When comparing patients aged 40-44(n = 382) and 45-49 years (n = 515), a strong increase in all parameters from 45 years was observed, with the ADR rising from 9.7% in patients aged 40-44 to 21.2% between 45 and 49(P <0.001) and the ANDR increasing from 3.1% in patients aged 40-44 to 6.4% in those aged 45-49 years(P < 0.03). With regard to patients aged 50-54(n = 849), a statistically significant increase in the ADR and ANDR was not observed between patients aged 45-49 and those aged 50-54 years. In the population with an average risk of CRC, the ADR and ANDR were still significantly higher in patients aged 45-49 compared with those aged 40-44 years.CONCLUSION This study shows a significant two-fold increase in the ADR and ANDR in patients aged 45 years and over.

Improved bowel preparation increases polyp detection and unmasks significant polyp miss rate

AIM: To retrospectively compare previous-day vs splitdose preparation in terms of bowel cleanliness and polyp detection in patients referred for polypectomy. METHODS: Fifty patients underwent two colonoscopies: one diagnostic in a private clinic and a second for polypectomy in a University Hospital. The latter procedures were performed within 12 wk of the index ones. Examinations were accomplished by two experienced endoscopists, different in each facility. Twenty-seven patients underwent screening/surveillance colonoscopy, while the rest were symptomatic. Previous day bowel preparation was utilized initially and splitdose for polypectomy. Colon cleansing was evaluated using the Aronchick scale. We measured the number of detected polyps, and the polyp miss rates per-polyp.RESULTS: Excellent/good preparation was reported in 38 cases with previous-day preparation(76%) vs 46 with split-dose(92%), respectively(P = 0.03). One hundred and twenty-six polyps were detected initially and 169 subsequently(P < 0.0001); 88 vs 126 polyps were diminutive(P < 0.0001), 25 vs 29 small(P = 0.048) and 13 vs 14 equal or larger than 10 mm. The miss rates for total, diminutive, small and large polyps were 25.4%, 30.1%, 13.7% and 6.6%, respectively. Multivariate analysis revealed that split-dose preparation was significantly associated(OR, P) with increased number of polyps detected overall(0.869, P < 0.001), in the right(0.418, P = 0.008) and in the left colon(0.452, P = 0.02). CONCLUSION: Split-dose preparation improved colon cleansing, enhanced polyp detection and unmasked significant polyp miss rates.

Sessile serrated adenoma detection rate is correlated with adenoma detection rate

AIM To investigated the association between adenoma detection rate(ADR) and sessile serrated ADR(SSADR) and significant predictors for sessile serrated adenomas(SSA) detection.METHODS This study is a retrospective, single-center analysis. Total colonoscopies performed by the gastroenterologists at the University of Tokyo Hospital between January and December 2014 were retrospectively identified. Polyps were classified as low-grade or high-grade adenoma, cancer, SSA, or SSA with cytological dysplasia, and the prevalence of each type of polyp was investigated. Predictors of adenoma and SSA detection were examined using logistic generalized estimating equation models. The association between ADR and SSADR for each gastroenterologist was investigated by calculating a correlation coefficient weighted by the number of each gastroenterologist's examination.RESULTS A total of 3691 colonoscopies performed by 35 gastroenterologists were assessed. Overall, 978 (26.5%) low-and 84 (2.2%) high-grade adenomas, 81 (2.2%) cancers, 66 (1.8%) SSAs, and 2 (0.1%) SSAs with cytological dysplasia were detected. Overall ADR was 29.5%(men 33.2%, women 23.8%) and overall SSADR was 1.8%(men 1.7%, women 2.1%). In addition, 672 low-grade adenomas (68.8% of all the detected lowgrade adenomas), 58 (69.9%) high-grade adenomas, 29 (34.5%) cancers, 52 (78.8%) SSAs, and 2 (100%) SSAs with cytological dysplasia were found in the proximal colon. Adenoma detection was the only significant predictor of SSA detection (adjusted OR: 2.53, 95%CI: 1.53-4.20; P < 0.001). The correlation coefficient between ADR and SSADR weighted by the number of each gastroenterologist's examinations was 0.606(P < 0.001).CONCLUSION Our results demonstrated that ADR is correlated to SSADR. In addition, patients with adenomas had a higher prevalence of SSAs than those without adenomas.

Relationship between diameter of split Hopkinson pressure bar and minimum loading rate under rock failure

In order to investigate the relationship between bar diameter and loading rate of the split Hopkinson pressure bar(SHPB) setup under the failure of rock specimen and realize the medium strain rate loading of specimen,new SHPB setups with different elastic bar's diameters of 22,36,50 and 75 mm were constructed.The tests were carried out on these setups at different loading rates,and the specimens had the same diameter of elastic bars and same ratio of length to diameter.The test results show that the larger the elastic bar's diameter is,the less the loading rate is needed to cause specimen failure,they show good power relationship,and that under the same strain rate loading,specimens are broken more seriously with larger diameter SHPB setup than with smaller one.

Dynamic tensile strength and failure mechanisms of thermally treated sandstone under dry and water-saturated conditions

To study the tensile strength and failure mechanisms of rock with hydro-thermal coupling damage under different loading rates,a series of static and dynamic splitting tests were conducted on thermally treated sandstone under dry and water-saturated conditions.Experimental results showed that high temperatures effectively weakened the tensile strength of sandstone specimens,and the P-wave velocity declined with increasing temperature.Overall,thermal damage of rock increased gradually with increasing temperature,but obvious negative damage appeared at the temperature of 100℃.The water-saturated sandstone specimens had lower indirect tensile strength than the dry ones,which indicated that water-rock interaction led to secondary damage in heat-treated rock.Under both dry and water-saturated conditions,the dynamic tensile strength of sandstone increased with the increase of strain rate.The water-saturated rock specimens showed stronger rate dependence than the dry ones,but the loading rate sensitivity of thermally treated rock decreased with increasing treatment temperature.With the help of scanning electron microscopy technology,the thermal fractures of rock,caused by extreme temperature,were analyzed.Hydro-physical mechanisms of sandstone under different loading rate conditions after heat treatment were further discussed.

Enzyme Inhibition Rate Method for Rapid Detection of Organophosphorus and Carbamate Pesticides in Cowpea

[Objectives ] The paper was to explore enzyme inhibition rate method for rapid detection of organophosphorus and carbamate pesticides in cowpea. [ Methods ] Acetylcholinesterase （ACHE） was added to cowpea extract, to determine the inhibition rate of extract against enzyme. The influences of different sampiing methods and sampling parts on detection results were compared. [ Results] The positive rate of standard sampling was 18.18% higher than that of non-stand- ard sampling, and the positive rate of samples collected from cowpea tail was 16.67% higher than that collected from other parts. [ Condmions] Enzyme inhibi- tion rate method is suitable for rapid detection of organophosphorus and carbamate pesticides in cowpea.

Interendoscopist variability in proximal colon polyp detection is twice higher for serrated polyps than adenomas

AIM To assess the interendoscopist variability in the detection of colorectal polyps according to their location and histological type.METHODS This study was a retrospective analysis of prospectively collected data from a regional colorectal cancer(CRC) screening program; 2979 complete colonoscopies from 18 endoscopists were included. Variability in performance between endoscopists for detection of at least one adenoma(A), one proximal adenoma(PA), one distal adenoma(DA), and one proximal serrated polyp(PSP) was assessed by using multilevel logistic regression models.RESULTS The observed detection rates among the 18 endoscopists ranged from 24.6% to 47.6%(mean = 35.7%) for A, from 19.1% to 39.0%(mean = 29.4%) for DA, from 6.0% to 22.9%(mean = 12.4%) for PA, and from 1.3% to 19.3%(mean = 6.9%) for PSP.After adjusting for patient-level variables(sex, age), the interendoscopist detection rates variability achieved a significant level for A, PA, and PSP but not for DA(P = 0.03, P = 0.02, P = 0.02 and P = 0.08, respectively). This heterogeneity, as measured by the variance partition coefficient, was approximately threefold higher for PA(6.6%) compared with A(2.1%), and twofold higher for PSP(12.3%) compared with PA.CONCLUSION These results demonstrate significant interendoscopist variability for proximal polyp particularly for serrated p o l y p s, b u t n o t f o r d i s t a l a d e n o m a d e t e c t i o n. These findings contribute to explain the decreased effectiveness of complete colonoscopies at preventing proximal CRCs and the need to carefully assess the proximal colon during scope procedure.

Implications of small-bowel transit time in the detection rate of capsule endoscopy: A multivariable multicenter study of patients with obscure gastrointestinal bleeding

AIMTo define the role of small-bowel transit time in the detection rate of significant small-bowel lesions.METHODSSmall-bowel capsule endoscopy records, prospectively collected from 30 participating centers in the Lombardy Registry from October 2011 to December 2013, were included in the study if the clinical indication was obscure gastrointestinal bleeding and the capsule reached the cecum. Based on capsule findings, we created two groups: P2 (significant findings) and P0-1 (normal/negligible findings). Groups were compared for age, gender, small-bowel transit time, type of instrument, modality of capsule performance (outpatients vs inpatients), bowel cleanliness, and center volume.RESULTSWe retrieved and scrutinized 1,433 out of 2,295 capsule endoscopy records (62.4%) fulfilling the inclusion criteria. Patients were 67 &#x000b1; 15 years old, and 815 (57%) were males. In comparison with patients in the P0-1 group, those in the P2 group (n = 776, 54%) were older (P &#x0003c; 0.0001), had a longer small-bowel transit time (P = 0.0015), and were more frequently examined in low-volume centers (P &#x0003c; 0.001). Age and small-bowel transit time were correlated (P &#x0003c; 0.001), with age as the sole independent predictor on multivariable analysis. Findings of the P2 group were artero-venous malformations (54.5%), inflammatory (23.6%) and protruding (10.4%) lesions, and luminal blood (11.5%).CONCLUSIONIn this selected, prospectively collected cohort of small-bowel capsule endoscopy performed for obscure gastrointestinal bleeding, a longer small-bowel transit time was associated with a higher detection rate of significant lesions, along with age and a low center volume, with age serving as an independent predictor.

Dynamic compressive property and failure behavior of extruded Mg-Gd-Y alloy under high temperatures and high strain rates

For the purpose of investigating the dynamic deformational behavior and failure mechanisms of magnesium under high strain rates,the Split Hopkinson Pressure Bar(SHPB)was used for investigating dynamic mechanical properties of extruded Mg-Gd-Y Magnesium alloy at ambient temperature(300 K),200℃(473 K)and 300℃(573 K)temperature.The samples after compression were analyzed by scanning electron microscope(SEM)and metallographic microscope.Dynamic mechanical properties,crack performance and plastic deformation mechanism of extruded Mg-Gd-Y Magnesium alloy along the extrusion direction(ED)were discussed.The results show that,extruded Mg-Gd-Y Magnesium alloy has the largest dynamic compressive strength which is 535 MPa at ambient temperature(300 K)and strain rate of 2826 s^(−1).When temperature increases,dynamic compressive strength decreases,while ductility increases.The dynamic compression fracture mechanism of extruded Mg-Gd-Y Magnesium alloy is multi-crack propagation and intergranular quasi-cleavage fracture at both ambient temperature and high temperature.The dynamic compressive deformation mechanism of extruded Mg-Gd-Y Magnesium alloy is a combination of twinning,slipping and dynamic recrystallization at both ambient temperature and high temperature.

Use of artificial intelligence in improving adenoma detection rate during colonoscopy:Might both endoscopists and pathologists be further helped

Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or precancerous lesions and the capacity to remove them intra-procedurally.Computeraided detection and diagnosis(CAD),thanks to the brand new developed innovations of artificial intelligence,and especially deep-learning techniques,leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy.The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate,and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality.Furthermore,a significant reduction in costs is also expected.In addition,the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule.The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy,as it is reported in literature,addressing evidence,limitations,and future prospects.

Image Tamper Detection and Multi-Scale Self-Recovery Using Reference Embedding with Multi-Rate Data Protection

This paper proposes a multi-scale self-recovery(MSSR)approach to protect images against content forgery.The main idea is to provide more resistance against image tampering while enabling the recovery process in a multi-scale quality manner.In the proposed approach,the reference data composed of several parts and each part is protected by a channel coding rate according to its importance.The first part,which is used to reconstruct a rough approximation of the original image,is highly protected in order to resist against higher tampering rates.Other parts are protected with lower rates according to their importance leading to lower tolerable tampering rate(TTR),but the higher quality of the recovered images.The proposed MSSR approach is an efficient solution for the main disadvantage of the current methods,which either recover a tampered image in low tampering rates or fails when tampering rate is above the TTR value.The simulation results on 10000 test images represent the efficiency of the multi-scale self-recovery feature of the proposed approach in comparison with the existing methods.