Recognition Of Disturbances Research Articles

The interdisciplinary field of Sleep Medicine-time for dentists to sink their teeth into it!

Sleep Medicine has evolved into a highly multidisciplinary field over the last few decades, involving respiratory physicians, neurologists, cardiologists, ENT surgeons, psychiatrists and psychologists to name a few. It is within this highly multidisciplinary context that we have seen an increasing role for dentists in the recognition, diagnosis and management of select sleep disturbances and disorders. Over the last couple of decades, this growing role for dentists has seen the informal emergence of a new interdisciplinary field of Dental Sleep Medicine-a field that bridges medicine and dentistry as it pertains to the diagnosis and management of sleep disorders. This article describes this new field in terms of its history, the evolving scope of practice for dentists, the implications for education and training, and the importance of multidisciplinary care that optimises outcomes for patients.

A method for disturbance identification in power quality based on cross-attention fusion of temporal and spatial features

The rapid growth of energy storage scale in power systems has posed higher demands on the monitoring and classification of power quality disturbances (PQDs). Therefore, a method based on cross-attention fusion of temporal and spatial features is proposed to improve the accuracy and robustness of fine classification of PQDs in this study.Firstly, utilizing the improved Sample Convolution and Interaction Model (SCINet) with its recursive downsampling-convolution-interaction architecture, and adding convolutional pooling layers to extract the temporal features of PQDs.Then, the improved VGG model is employed to extract the spatial features of PQDs. Finally, by introducing a cross-attention mechanism to capture the correlation and interaction between the temporal and spatial features, the representation ability of the features is enhanced to achieve disturbance recognition. This study is based on IEEE standards and mathematical models, and generates 25 types of PDQ data through Python simulation for training and testing.The proposed method achieves an average classification accuracy of 95.01 % in a high-noise environment of 20 dB, as well as compared with other mainstream deep learning models and verified through ablation study. Furthermore, the classification test of 10 types of PQDs sampled on a hardware platform demonstrates an average accuracy of 99.66 %, further validating the reliability of the proposed method.

Recognition of Multiple Power Quality Disturbances Based on Discrete Wavelet Transform and Improved Long Short-Term Memory Networks

In the past few years, distributed electricity generation from renewable sources, or microgrid systems, has been connected to the grid to increase power supply stability. This responds to government policy regarding commitment to using 100% renewable energy in operations (RE100) project efforts. This results in the entry of power electronic or non-linear equipment into the electrical system, making it more sensitive. Moreover, multiple power quality disturbances (PQDs) consist of a variety of single disturbances. Analysis of complex multi-label patterns is a challenging task. In this paper, we propose a methodology to address this challenge by leveraging Discrete Wavelet Transform (DWT) and Improved Long Short-Term Memory Networks (LSTM). Firstly, multiple PQDs are synthesized utilizing a mathematical model based on IEEE standards 1159-2019. Secondly, the obtained PQDs are decomposed into nine feature classes, yielding detailed (cDs) and approximation (cAs) coefficients through Five-Level DWT Decomposition. Furthermore, we conducted a comparative analysis of each component across five different wavelet functions: haar1, db4, bior1.3, coif2, and sym4. Thirdly, the cDs and cAs coefficients derived from each wavelet type undergo statistical analysis before being inputted into the LSTM model for classification of each feature class. Our results highlight that cD5 components obtained from the db4 wavelet exhibit the highest accuracy rate of 93.86%. This finding elucidates the significance of selecting appropriate wavelet types and compositions for the successful classification of multiple PQDs.

An intelligent complex power quality disturbance recognition method based on two dimension encoding conversion and machine vison

This study proposes an intelligent methodology for accurate identification of complex power quality disturbance (PQD) based on 2D encoding conversion and machine vision. Firstly, a 2D image encoding method as improved Markov transition field (IMTF) is proposed to convert 1D PQD signals to 2D images, which can realize the enhancement of disturbance feature display. It can solve the information loss and duplication problems of traditional Markov transition field (MTF). Then, a new classification method AFF-ResNetXt50 is proposed to realize the PQD accurate recognition. It can address the problem that traditional ResNetXt50 is susceptible to the interference from irrelevant information and thus severely degrades the classification accuracy. Compared with other popular methods, this proposed method can obtain higher accuracy as 98.5 % for PQD signals recognition. Finally, PQD experimental platform is established, and IEEE PES dataset is also used to certify the proposed method has the excellent performances and robustness.

A novel recognition method for complex power quality disturbances based on Markov transition field and improved densely connected network

In order to solve the difficulty that complex power quality disturbances (PQDs) are difficult to recognize accurately and efficiently under the new power system background, this paper proposes a novel PQDs recognition method based on markov transition field (MTF) and improved densely connected network (DenseNet). Firstly, the one-dimensional PQDs signal is mapped into the two-dimensional image with clear texture features by using MTF encoding method. Then, a DenseNet-S lightweight network is designed and the convolutional attention module (CBAM) is introduced to improve its feature extraction ability, so as to enhance the performance of the network. Finally, the images are input into the improved model for training and learning, and PQDs recognition is realized through the optimal model. In order to verify the effectiveness of the proposed method, experimental tests are carried out based on IEEE 1159 standard simulation dataset and real-world field measured signals dataset, and compared with existing recognition methods. The results show that the proposed method can effectively improve the recognition accuracy and noise robustness of complex PQDs, and has more advantages in disturbances recognition efficiency. It can meet the recognition accuracy and efficiency requirements of massive and complex PQDs events in engineering applications.

Identification algorithm for power quality disturbance of inspection robot in converter station during operation

AbstractAs power quality disturbances become increasingly complex, it is imperative that the speed and accuracy of the inspection robot at the converter station be improved. For this purpose, this study designs a power quality disturbance feature extraction method based on the fast‐adaptive S‐transform. This method preserves the main feature information and eliminates redundant calculations on the basis of adaptive transform. On this basis, a power quality disturbance identification model built on a multi label lightweight gradient elevator is constructed. In the experimental results, compared to the generalized S‐transform, adaptive S‐transform, and S‐transform, the total extraction time of the proposed method was reduced by 96.09%, 91.56%, and 91.22%, respectively. The average accuracy of extracting features for a single disturbance was 99.56%, while for complex disturbance, it was 98.24%, both of which outperformed the comparison algorithms. It is verified that the proposed method can improve the extraction accuracy of power quality disturbance signal. In the recognition of a single disturbance signal, the constructed model exhibited a high accuracy of over 99%. In recognizing composite disturbance signals, the model demonstrated high accuracy and strong stability. Its effectiveness has been confirmed through experiments. The paper aims to enhance the speed and accuracy of power quality disturbance recognition algorithms. This will assist inspection robots working in converter stations, and ensure stable and safe operation of the power grid.

A new method for recognition and classification of power quality disturbances based on IAST and RF

A new method for recognition and classification of power quality disturbances based on IAST and RF

Complex Power Quality Disturbance Recognition Research Based on Deep Complementary Fusion of 2-D Coding Transition

Complex Power Quality Disturbance Recognition Research Based on Deep Complementary Fusion of 2-D Coding Transition

Embedded Parallel Computing Platform for Real-Time Recognition of Power Quality Disturbance Based on Deep Network

Embedded Parallel Computing Platform for Real-Time Recognition of Power Quality Disturbance Based on Deep Network

The Impact of Immunotherapy on Sleep and Circadian Rhythms in Patients with Cancer.

Immunotherapy has revolutionized treatments for both early and advanced cancers, and as their role evolves, their impact on sleep and circadian rhythms continues to unfold. The recognition, evaluation, and treatment of sleep and circadian rhythm disturbance leads to improved symptom management, quality of life and treatment outcomes. An intricate complex relationship exists in the microenvironment with immunity, sleep and the tumor, and these may further vary based on the cancer, addition of standard chemotherapy, and pre-existing patient factors. Sleep and circadian rhythms may offer tools to better utilize immunotherapy in the care of cancer patients, leading to better treatment outcome, reduced symptom burden, and increased quality of life.

Power Quality Disturbance Recognition based on Deep Neural Network and Adaptive Feature Fusion

Aiming at the problems of low recognition accuracy and low noise resistance of current power quality disturbance recognition algorithms, a power quality disturbance recognition method based on the fusion of neural network and adaptive features is proposed. Firstly, one-dimensional features are extracted by 1D_CNN+GRU network. The GADF algorithm and 2D_CNN are used to extract 2D features, and then the extracted 1D features and 2D features are adaptively fused into a new feature. Finally, the new features are input into the channel attention mechanism and classified through the full connection layer. The experimental results show that the classification accuracy of the proposed method is above 92% for 40 disturbance types containing single, double, triple and quadruple disturbances, and above 96% for 20 disturbance types containing single and double disturbances.

Investigation of the Relationship Between Mental Health Status and Sleep Patterns of Children Who Experienced the Kahramanmaraş Earthquake: Data From the Earthquake Region.

The psychological conditions of children exposed to earthquakes, such as somatization, obsessive-compulsive, hostility, and paranoid thought, have not been adequately studied. This study was conducted to examine the relationship between the mental states and sleep patterns of children who experienced the Kahramanmaraş earthquake. This is a descriptive, cross-sectional, and relational study that included 867 children from 11 provinces of Turkey affected by the earthquake that occurred in Kahramanmaraş on February 6th. The Child Follow-up Form, Mental Symptom Screening Scale, and Child Sleep Habits Scale were used to collect data. The psychological symptoms (anxiety, depression, somatization, anger-hostility, obsessive-compulsive symptoms, interpersonal sensitivity, phobic anxiety, paranoid ideation, psychotic symptoms, and other issues) experienced by children after the earthquake explained 96.9% of their sleep disturbance. Some of the demographic characteristics, such as age, the status of experiencing loss in the earthquake, the survival status of their mother and father, the survival status of their siblings, the total number of losses after the earthquake, the total number of losses in the nuclear family, total hours spent under the rubble, the hours children waited for their father, mother, and siblings to come out of the rubble, accounted for 77.9% of the mental symptoms (SCL-90). These results showed the importance of early recognition and treatment of sleep disturbances to prevent possible psychiatric disorders after children have been exposed to natural disasters. It is of great importance to evaluate the mental states of children after the earthquake and to apply appropriate psychiatric interventions.

Fiber-Optic Telecommunication Network Wells Monitoring by Phase-Sensitive Optical Time-Domain Reflectometer with Disturbance Recognition

The paper presents the application of a phase-sensitive optical time-domain reflectometer (phi-OTDR) in the field of urban infrastructure monitoring. In particular, the branched structure of the urban network of telecommunication wells. The encountered tasks and difficulties are described. The possibilities of usage are substantiated, and the numerical values of the event quality classification algorithms applied to experimental data are calculated using machine learning methods. Among the considered methods, the best results were shown by convolutional neural networks, with a probability of correct classification as high as 98.55%.

Frequency of Acute Kidney Injury with Hypoxic Ischemic Encephalopathy Staging in Neonates with Perinatal Asphyxia: An Observational Study (Descriptive Case Series)

Perinatal asphyxia affects almost all the organs in neonates, kidneys being one of commonly affected organs. Early recognition of disturbances in renal function is important for early intervention to prevent irreversible damage to kidneys. Objective: In this study we observed the frequency of acute kidney injury (AKI) in neonates with clinical staging of hypoxemic ischemic encephalopathy (HIE) in neonates with perinatal asphyxia. Methodology: An overall number of 145 term neonates with perinatal asphyxia admitted at nursery of Liaquat University Hospital, Hyderabad from February-2019 to july-2019 were included in this study. Urine output was monitored from day one and serum creatinine was sent on third day of life. Serum creatinine was sent early of those neonates having low urine output. Results: The average birth weight was 2.72±2.67 kg. There were 106 (73.1%) males and 39 (26.9%) females. Most neonates were born via simple vaginal delivery 100 (68.97%) and few via caesarian section 45 (31.03%). Out of 145 neonates 38 (26.21%) were in stage I, 95 (65.52%) in stage II and 12 (8.28%) in stage III HIE. Frequency of AKI in neonates with perinatal asphyxia was 55 (37.93%). Incidence of AKI was significantly high in stage II 45(81.8%) as compare to stage I 0(0%) and stage III 10(18.2%) (p=0.0005). Conclusion: AKI is a common complication of perinatal asphyxia. Further multicenter based and studies on large scale need to be done to identify the inter-relationship between maternal factors and perinatal asphyxia associated AKI to prevent neonatal morbidity and mortality. Keywords: Acute kidney injury, perinatal asphyxia, hypoxic ischemic encephalopathy.

A fused CNN‐LSTM model using FFT with application to real‐time power quality disturbances recognition

AbstractWith the progress of renewable energy generation and energy storage technologies, more and more renewable sources and devices are integrated into the power system. Due to the complexity of the power system, single and multiple power quality disturbances (PQDs) occur more frequently. Hence, real‐time detection of PQDs is the primary issue to mitigate the risk of distortions. This study presents the real‐time PQDs classification using fused convolutional neural networks (CNN) combined with long short‐term memory (fused CNN‐LSTM) architecture based on time and frequency domain features. The frequency‐domain features were obtained from time‐series data using fast Fourier transform. The original time‐domain and frequency‐domain features are extracted by respective CNN‐LSTM structures. The extracted time and frequency domain features are concatenated to classify the PQD through fully connected layers. Our proposed method was trained and tested using 16 types of synthetic noise PQDs data generated by mathematical models, in accordance with the standard IEEE‐1159. Moreover, to further verify the performance of our approach, a simulation distributed power system is carried out to detect various PQDs. We compared three advanced neural network approaches: Deep CNN, CNN‐LSTM, and multifusion CNN (MFCNN). The fused CNN‐LSTM model takes only 0.64 ms to classify each PQDs signal and achieves an accuracy of 98.95% and 98.89% in synthetic data and simulated power systems which indicates our proposed method outperformed compared methods.

Automatic Recognition of Constant-Frequency Electromagnetic Disturbances Observed by the Electric Field Detector on Board the CSES

Since the CSES (China Seismo-Electromagnetic Satellite) has been in orbit, it has detected a large number of constant-frequency electromagnetic disturbances (CFEDs), which are horizontal lines on the spectrum. In this paper, we present an algorithm for automatic recognition of CFEDs based on computer vision technology. The relevant results are of great significance for analysis of perturbation events and mining of the transformation laws of global space events. First, a grayscale spectrogram is obtained; then, a horizontal convolution kernel is used to enhance the horizontal edge features of the grayscale graph, and finally, black-and-white binarization is performed to complete data preprocessing. The preprocessed data are then fed into an unsupervised cluster model for training and recognition to realize automatic recognition of CFEDs. Experimental results show that the CFED recognition algorithm proposed in this paper is effective, with a recognition accuracy of more than 98%.

Recognition of Hybrid PQ Disturbances Based on Multi-Resolution S-Transform and Decision Tree

Aiming at the problems of multiple types of power quality composite disturbances, strong feature correlation and high recognition error rate, a method of power quality composite disturbances identification based on multi-resolution S-transform and decision tree was proposed. Firstly, according to IEEE standard, the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given, and the disturbance waveform samples are generated in batches. Then, in order to improve the recognition accuracy, the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis. On this basis, five disturbance time-frequency domain features are extracted, which quantitatively reflect the characteristics of the analyzed power quality disturbance signal, which is less than the traditional method based on S-transform. Finally, three classifiers such as K-nearest neighbor, support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances. Simulation results show that the classification accuracy of decision tree algorithm is higher than that of K-nearest neighbor and support vector machine. Finally, the proposed method is compared with other commonly used recognition algorithms. Experimental results show that the proposed method is effective in terms of detection accuracy, especially for combined PQ interference.

An Enterprise of Cognitive Fog Computing For Disturbance Recognition in Internet of Things

The Internet of Things (IoT) is a cutting-edge piece of cybernetic infrastructure that will eventually link all manner of previously disconnected physical objects to the web. The IoT is rapidly expanding into many facets of human life. IoT's attack surface has grown as a result of the technology's hyper-connectivity and inherent heterogeneity. In addition, IoT devices are used in both managed and unmanaged settings, leaving them open to innovative attacks. Fog computing is used in the proposed intrusion detection system for IoT applications to implement intrusion detection in a decentralised manner. Attack detection at fog nodes and summarization on a cloud server make up the proposed system's two parts. The local fog nodes in the IoT environment examine the traffic, and then they send a report to the cloud server that summarises the current global security state of the IoT application. According to the results of the experiments, the fog nodes are able to identify the attack 27% more quickly while also reducing the number of false alarms. The work that has been recommended provides a beginning point for the creation of a fog-based intrusion detection system that can be used for applications related to the IoT. The proposed system has a false alarm rate of only 0.32% and an accuracy of 98.15 percent. The proposed method can only identify attacks that conform to specific patterns.

Production Disturbances Recognition Model and Adaptive Energy-saving Control Method of Discrete Manufacturing Unit

Production Disturbances Recognition Model and Adaptive Energy-saving Control Method of Discrete Manufacturing Unit

Sleep disturbances in autoimmune encephalitis.

Autoimmune encephalitis is an inflammatory neurological disorder characterized by psychiatric symptoms, cognitive impairment, and focal neurological deficits or seizures. Sleep disturbances are not a major consideration in the diagnosis and treatment of patients with autoimmune encephalitis. Various types of sleep disturbances are frequent, severe, and long-lasting, which can compromise the recovery and quality of life in patients with autoimmune encephalitis. Sleep disorders in patients with autoimmune encephalitis have received limited attention, and the prevalence and pathophysiological mechanisms of sleep disorders remain unclear. Recent studies have suggested that early recognition of specific sleep disturbances may provide clues for diagnosing autoimmune encephalitis. Furthermore, early diagnosis and treatment of sleep disturbances can promote recovery and improve long-term outcomes in patients with autoimmune encephalitis. In this report, we aimed to provide a comprehensive and extensive understanding of the clinical relevance of autoimmune encephalitis and specific related sleep disorders.