Abnormal State Research Articles

Local outlier factor based condition monitoring for sub-module capacitors in modular multilevel converters

Modular multilevel converters (MMC) as a key technology for future power grids is rapidly developing. The failure rate of sub-module (SM) capacitors is only lower than that of power devices in MMC, so accurate evaluation of the reliability of capacitors is crucial for the safe operation of MMC. Both the increase of equivalent series resistance (ESR) and decrease of equivalent series capacitance (ESC) are features for capacitor deteriorating. ESC or ESR of the capacitor reaching to limited values indicates that the end of its lifespan and it must be replaced. Most of contemporary researches only focus on ESC or ESR. This article considering the characteristics of both ESC and ESR, proposes a method for monitoring the status of SM capacitors based on machine learning algorithm: local outlier factor (LOF). By utilizing the existing SM switching function and capacitor voltage signals within a fundamental frequency cycle in the control system of MMC, the proposed strategy can not only monitor the faulty capacitors, but also can sort the degrees of abnormal states of capacitors, and the strategy is applicable to MMC with single and multiple types of SM topologies. The proposed method is verified by the hybrid MMC model on Matlab/Simulink.

High Expression of miR-6785-5p in the Serum Exosomes of Psoriasis Patients Alleviates Psoriasis-Like Skin Damage by Interfering with the MNK2/p-eIF4E Axis in Keratinocytes.

Psoriasis is a chronic inflammatory skin disease characterized by abnormal keratinocyte proliferation and inflammation. MiRNAs and serum exosomes participate in the pathogenesis of many diseases. The objective of this study is to explore the function of miR-6785-5p in psoriatic keratinocytes and its upstream and downstream mechanisms. For our study, we employed qRT-PCR and fluorescence in situ hybridization to evaluate miR-6785-5p in psoriatic keratinocytes and conducted a microRNA microarray for identifying differentially expressed miRNAs in patient serum exosomes. We then cocultured keratinocytes with these exosomes, using immunofluorescence staining and qRT-PCR to assess uptake and miR-6785-5p overexpression. We explored miR-6785-5p's role through transfection with specific mimics and inhibitors and confirmed MNK2 as its target using a luciferase assay. MNK2's function was further examined using siRNA technology. Lastly, we applied an imiquimod-induced psoriasis mouse model, also employing siRNA, to investigate MNK2's role in psoriasis. MiR-6785-5p demonstrates a notable overexpression in the keratinocytes of psoriasis patients as well as in their serum exosomes. These keratinocytes actively uptake the miR-6785-5p-enriched serum exosomes. Functionally, miR-6785-5p appears to alleviate psoriasis-like skin damage, observable both in vitro and in vivo, by downregulating MNK2 expression. Psoriasis keratinocytes uptake serum exosomes highly expressing miR-6785-5p. MiR-6785-5p inhibits the abnormal proliferation and inflammatory state of keratinocytes by reducing MNK2 expression and interfering with the MNK2/p-eIF4E axis.

A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens

Understanding the subcellular localization of lncRNAs is crucial for comprehending their regulation activities. The conventional detection of lncRNA subcellular location usually uses in situ detection techniques, which are resource intensive. Some machine learning-based algorithms have been proposed for lncRNA subcellular location prediction in mammals. However, due to the low level of conservation of lncRNA sequence, the performance of cross-species models remains unsatisfactory. In this study, we curated a novel dataset containing subcellular location information of lncRNAs in Homo sapiens. Subsequently, based on the BERT pre-trained language algorithm, we developed a model for lncRNA subcellular location prediction. Our model achieved a micro-average area under the receiver operating characteristic (AUROC) of 0.791 on the training set and an AUROC of 0.700 on the testing nucleus set. Additionally, we conducted cross-species validation and motif discovery to further investigate underlying patterns. In summary, our study provides valuable guidance and computational analysis tools for exploring the mechanisms of lncRNA subcellular localization and the dynamic spatial changes of RNA in abnormal physiological states.

Effects of physical fitness on mental health of Chinese college students: across-sectional study

BackgroundThe physical and mental health of college students is often mentioned, but there is limited research on a direct relationship between the mental health status of college students and their physical fitness level. This study mainly proves the relationship between Chinese college students’ mental health and physical fitness indicators.MethodThis study collected SCL-90 Scale test results from 5262 students (4012 boys and 1250 girls) through a questionnaire survey and conducted a Sport Quality Test on these students. Statistical software SPSS was applied for differential analysis and logistic regression analysis. Specifically, the differences in sport quality indicators between normal and abnormal mean total scores of psychological tests were analyzed first. Then, the binary logistic regression model was used to explore the impacts of sports quality indicator scores on students’ psychological fitness.ResultsThere are differences in the results of physical fitness tests between students with abnormal psychology and students with normal psychology. The four indexes of students’ vital capacity, speed, explosive power of lower limbs, and endurance running are effective in improving students’ psychologically abnormal state, and endurance running and improving vital capacity are the most effective methods to improve students’ psychologically abnormal state. In the physical tests of Chinese college students, the risk of psychological abnormalities was reduced by 9% for every one-point increase in lung capacity and 10.4% for every one-point increase in endurance running performance.ConclusionsChinese college students’ physical fitness and mental health are related. The best methods for treating psychological disorders are lung capacity improvement and endurance running. According to the physical test results of Chinese college students, for every 1-point increase in lung capacity and endurance running, the risk of psychological abnormalities decreased by 9% and 10.4%, respectively.

Early State Prediction Model for Offshore Jacket Platform Structural Using EfficientNet-B0 Neural Network

Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.

Aberrant thalamocortical connectivity and shifts between the resting state and task state in patients with schizophrenia.

Prominent pathological hypotheses for schizophrenia include auditory processing deficits and dysconnectivity within cerebral networks. However, most neuroimaging studies have focused on impairments in either resting-state or task-related functional connectivity in patients with schizophrenia. The aims of our study were to examine (1) blood oxygen level-dependent (BOLD) signals during auditory steady-state response (ASSR) tasks, (2) functional connectivity during the resting-state and ASSR tasks and (3) state shifts between the resting-state and ASSR tasks in patients with schizophrenia. To reduce the functional consequences of scanner noise, we employed resting-state and sparse sampling auditory fMRI paradigms in 25 schizophrenia patients and 25 healthy controls. Auditory stimuli were binaural click trains at frequencies of 20, 30, 40 and 80 Hz. Based on the detected ASSR-evoked BOLD signals, we examined the functional connectivity between the thalamus and bilateral auditory cortex during both the resting state and ASSR task state, as well as their alterations. The schizophrenia group exhibited significantly diminished BOLD signals in the bilateral auditory cortex and thalamus during the 80 Hz ASSR task (corrected p < 0.05). We observed a significant inverse relationship between the resting state and ASSR task state in altered functional connectivity within the thalamo-auditory network in schizophrenia patients. Specifically, our findings demonstrated stronger functional connectivity in the resting state (p < 0.004) and reduced functional connectivity during the ASSR task (p = 0.048), which was mediated by abnormal state shifts, within the schizophrenia group. These results highlight the presence of abnormal thalamocortical connectivity associated with deficits in the shift between resting and task states in patients with schizophrenia.

Automatic Detection of Abnormal Drone Flight State by Measuring Changes in Images Captured by a Drone-Mounted Camera

Automatic Detection of Abnormal Drone Flight State by Measuring Changes in Images Captured by a Drone-Mounted Camera

IDENTIFICATION OF ROOTKITS IN NETWORK TRAFFIC WITH USING THE BAGGING OF CLASSIFIERS

The paper proposes an approach to identify anomalies in network traffic based on the use of machine learning classifiers. The solution allows you to determine the resulting state class by averaging the votes of individual classifiers. The approach was evaluated on the NSL-KDD public dataset. A compar- ison of the performance of classifiers and their averaged evaluation using the Weka tool was performed. The NSL-KDD set has been optimized, with an emphasis on "rootkit" type attacks, as one of the most diffi- cult types of attacks to detect. Using the bagging-based approach implemented in the Weka application, it was possible to obtain accuracy results – 99.94%. During the experiment, a tendency of increasing accu- racy in the application of bagging on open data was revealed as the volume of training data increases. The proposed approach can be applied in the design of systems for detecting attacks and other abnormal states of information systems. The results of the accuracy of the average assessment require further re- search in order to improve the indicators. It is possible to modernize the approach of averaging the votes of classifiers by excluding/adding other classifiers, qualitative selection of attributes and their features, in- creasing the number of training samples for classification.

Abnormal state prediction of flotation process based on dual attention mechanism and multivariate information fusion

AbstractPredicting abnormal conditions in flotation processes is vital for safety, efficiency, and product quality. However, existing studies lack predictions of abnormal working conditions in flotation processes and neglect temporal information in data. To address this, this paper proposes a novel approach for predicting abnormal work conditions in flotation processes. It utilizes a dual attention mechanism and multivariate information fusion. Features are extracted from froth images using the Xception model, a pre‐trained convolutional neural network. These features are combined with flotation process monitoring variables, creating fused data. An encoder and decoder time feature seq2seq (EDTF‐seq2seq) model with time and feature attention modules enables end‐to‐end information fusion and work condition prediction. The attention modules assign weights to each feature point, capturing the time–feature relationship and improving prediction accuracy. Four sets of experiments using real flotation process data validate the effectiveness of the proposed method, achieving favourable prediction accuracy.

Enhanced heat transfer and reduced flow reversals in turbulent thermal convection with an obstructed centre

We report an experimental study about the effect of an obstructed centre on heat transport and flow reversal by inserting an adiabatic cylinder at the centre of a quasi-two-dimensional Rayleigh–Bénard convection cell. The experiments are carried out in a Rayleigh number ( $Ra$ ) range of $2\times 10^7 \leq Ra \leq 2\times 10^9$ and at a Prandtl number ( $Pr$ ) of $5.7$ . It is found that for low $Ra$ , the obstructed centre leads to a heat transfer enhancement of up to 21 $\%$ , while as $Ra$ increases, the magnitude of the heat transfer enhancement decreases and the heat transfer efficiency ( $Nu$ ) eventually converges to that of the unobstructed normal cell. Particle image velocimetry measurements show that the heat transfer enhancement originates from the change in flow topology due to the presence of the cylindrical obstruction. In the low- $Ra$ regime the presence of the obstruction promotes the transition of the flow topology from the four-roll state to the abnormal single-roll state then to the normal single-roll state with increasing obstruction size. While in the high- $Ra$ regime, the flow is always in the single-roll state regardless of the obstruction size, although the flow becomes more coherent with the size of the obstruction. We also found that in the presence of the cylindrical obstruction, the stability of the corner vortices is significantly reduced, leading to a large reduction in the frequency of flow reversals.

A novel exponential model for tool remaining useful life prediction

Implementing proactive maintenance strategies based on condition prediction for cutting tools can reduce expensive, unscheduled maintenance events. This work proposes an novel exponential model to predict the Remaining Useful Life (RUL) of cutting tools. Firstly, a new monitoring indicator named second-order derivative of health index (SDHI) is constructed, and on this basis, a 3σ interval-based first predicting time (FPT) adaptive selection method is proposed to correlate the observable SDHI with the unobservable tool wear rate, automatically determines the abnormal tool wear state without human intervention. Secondly, the integration of the Bayesian inference mechanism with the expectation maximization (EM) algorithm enables the achieving of real-time iterative updates for model parameters. Thirdly, To reduce stochastic errors while predicting the RUL, particle filtering and probability density function (PDF) are applied to handle prediction uncertainty. The experimental findings obtained from the milling experiments demonstrate that the proposed model exhibits robust adaptability to various cutting conditions, thereby leading to enhanced RUL prediction performance.

Dynamic fuzz testing of UAV configuration parameters based on dual guidance of fitness and coverage

ArduCopter's configuration parameter verification defects may cause the Unmanned Aerial Vehicle (UAV) in abnormal status. However, traditional UAV configuration parameter defect detection methods based on fuzz testing lack guidance design and inadequately detect configuration parameter defects. This paper proposes an improved configuration security defect analysis method based on fuzz testing. Using the fitness feedback mechanism based on the CAG neural network to guide the generation of fuzz testing cases, and using multiple coverage feedback mechanisms to guide the exploration direction of fuzz testing. Experimental results show that this method almost covers ArduCopter's position and attitude controller, guiding the UAV into abnormal states such as spin and crash, and detecting specific instances of configuration parameter defects.

Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF6 gas flow, and O2 gas flow were collected during a silicon etching process with SF6, O2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes.

Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration, etc. These data have the characteristics of real-time, scale and diversity, and need to be processed and analyzed by appropriate algorithms. On the basis of previous studies, this project summarized the application of various machine learning algorithms in device state detection, compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis, calculated the evaluation parameters of MSE, RMSE, MAE, MAPE, R² and other aspects of the machine learning regression model. Compare the effects of various regression models for better monitoring and prediction of equipment status. Through the analysis of a large number of historical data, different equipment state models can be established, and these models can be used to monitor and predict the current equipment state. This can effectively avoid production line downtime or other losses caused by equipment failures or abnormalities. At the same time, through the in-depth analysis of historical data, we can find some potential problems and take corresponding measures to prevent them. This project aims to summarize the application of various machine learning algorithms in device status detection, compare and contrast the differences of various machine learning algorithms in sensor device detection, realize efficient processing and analysis of sensor data, calculate MSE, RMSE, MAE, MAPE, R² and other evaluation parameters, and evaluate and compare each model. To provide more accurate, reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.

Extremely compact and lightweight triboelectric nanogenerator for spacecraft flywheel system health monitoring

The abnormal state of supporting bearings significantly affects the directional accuracy of spacecraft flywheel systems. A self-sensing triboelectric nanogenerator (TENG) offers a desirable route for on-orbit health monitoring and can potentially improve the intelligence level of spacecraft. Here, an extremely compact and lightweight TENG (CL-TENG) for the nonguided clearance of bearings is proposed and fabricated for the condition monitoring of a flywheel assembly. Using the bearing radial space allows the CL-TENG to perceive the revolution and whirling behavior of the cage; therefore, the output of the CL-TENG is simultaneously affected by the bearing speed and load. A test platform for capturing the kinematic of the cage is established to validate the effectiveness of the CL-TENG based on bearing skidding, cage rotation, and whirling instability. A negative exponential correlation between the output voltage and dynamic whirling clearance index is shown to be the basis of cage whirling sensing. The internal clearance and materials of the CL-TENG are optimized to enhance the output performance while preventing wear on the flexible electrodes. The application of the CL-TENG to an actual flywheel system in a simulated space environment and nonstationary operating conditions demonstrate its advantages in detecting the abnormal operating state of the cage.

Altered dynamic functional and effective connectivity in drug-naive children with Tourette syndrome.

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by repetitive, stereotyped, involuntary tics, the neurological basis of which remains unclear. Although traditional resting-state MRI (rfMRI) studies have identified abnormal static functional connectivity (FC) in patients with TS, dynamic FC (dFC) remains relatively unexplored. The rfMRI data of 54 children with TS and 46 typically developing children (TDC) were analyzed using group independent component analysis to obtain independent components (ICs), and a sliding-window approach to generate dFC matrices. All dFC matrices were clustered into two reoccurring states, the state transition metrics were obtained. We conducted Granger causality and nodal topological analyses to further investigate the brain regions that may play the most important roles in driving whole-brain switching between different states. We found that children with TS spent more time in state 2 (PFDR < 0.001), a state characterized by strong connectivity between ICs, and switched more quickly between states (PFDR = 0.025) than TDC. The default mode network (DMN) may play an important role in abnormal state transitions because the FC that changed the most between the two states was between the DMN and other networks. Additionally, the DMN had increased degree centrality, efficiency and altered causal influence on other networks. Certain alterations related to executive function (r = -0.309, P < 0.05) and tic symptom ratings (r = 0.282; 0.413, P < 0.05) may represent important aspects of the pathophysiology of TS. These findings facilitate our understanding of the neural basis for the clinical presentation of TS.

Abnormal states with unequal constituent masses

The Bethe–Salpeter equation for system of two oppositely charged particles not only reproduces the Coulomb spectrum, but, for enough large coupling constant C>π4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal C}>\\frac{\\pi }{4}$$\\end{document}, predicts additional levels not predicted by the Schrödinger equation. These relativistic states (called abnormal), in contrast to the normal ones, are dominated, for more than 90–99%, by Fock states involving the exchange particles—the photons, whereas contribution of two massive charged particles themselves is rather small (1–10%). Since the carrier of a large (positive) charge is a heavy ion, and the negative charge is provided by electron, the masses of two constituents are very different. It is shown that in a system with so different masses the abnormal states still exist. Moreover, the effect of unequal masses is attractive. The balance between photons and charged constituents is weakly sensitive to the mass ratio, so the photons still predominate.

An enhanced perception health state assessment method for subway sliding plug door transmission system

The monitoring data (current and rotational speed) of the subway sliding plug door transmission system changed positively and negatively with the acceleration and weight of the door. How to perceive the changes is a challenging problem in the health state assessment of transmission system. To address this problem, an enhanced perception health state assessment method was proposed for the transmission systems. In the method, firstly, the equivalent resistance force is calculated by monitoring the current and rotational speed data according to mechanical dynamic knowledge. Secondly, the sensitive features of normal and abnormal states are screened out from the enhanced dataset constructed by current, rotational speed data, and equivalent resistance force data. Finally, the health state of the transmission system is assessed using an integrated learning algorithm. The effectiveness of the method is verified by benchmark experimental data, and the results indicate that the method has a higher accuracy with four classifiers and a broader suitable range with varying door acceleration and weight.

Weakly nonlinear analysis on synchronization and oscillation quenching of coupled mechanical oscillators

Various oscillatory phenomena occur in the world. Because some are associated with abnormal states (e.g. epilepsy), it is important to establish ways to terminate oscillations by external stimuli. However, despite the prior development of techniques for stabilizing unstable oscillations, relatively few studies address the transition from oscillatory to resting state in nonlinear dynamics. This study mainly analyzes the oscillation-quenching of metronomes on a platform as an example of such transitions. To facilitate the analysis, we describe the impulsive force (escapement mechanism) of a metronome by a fifth-order polynomial. By performing both averaging approximation and numerical simulation, we obtain a phase diagram for synchronization and oscillation quenching. We find that quenching occurs when the feedback to the oscillator increases, which will help explore the general principle regarding the state transition from oscillatory to resting state. We also numerically investigate the bifurcation of out-of-phase synchronization and beat-like solution. Despite the simplicity, our model successfully reproduces essential phenomena in interacting mechanical clocks, such as the bistability of in-phase and anti-phase synchrony and oscillation quenching occurring for a large mass ratio between the oscillator and the platform. We believe that our simple model will contribute to future analyses of other dynamics of mechanical clocks.

HVAC early fault detection using a fuzzy logic based approach

The need for improving the energy efficiency of existing buildings has driven to the implementation of building energy management systems (BEMS) that can help facilities manager to discover and identify problems that may cause energy wastage or affect to occupants’ comfort. Modern data-driven fault detection and diagnosis (FDD) make use of the data collected by the building BEMS to provide high accuracy in the revelation of heating, ventilation and air-conditioning (HVAC) system faults. However, these methods need a large amount of faulty data samples during the training, which is an uncommon situation in the real world. The main focus of this paper is to present a methodology to detect faults when the number of faulty samples is low. For this purpose, a regression-based methodology based on an adaptative neuro-fuzzy inference system (ANFIS) chiller model is developed using the data collected from a real use case. The model presents good results, that can be used for benchmarking the machine operation and detect the abnormal operation states.