Online learning fuzzy echo state network with applications on redundant manipulators.

Redundant manipulators are universally employed to save manpower and improve work efficiency in numerous areas. Nevertheless, the redundancy makes the inverse kinematics of manipulators hard to address, thus increasing the difficulty in instructing manipulators to perform a given task. To deal with this problem, an online learning fuzzy echo state network (OLFESN) is proposed in the first place, which is based upon an online learning echo state network and the Takagi-Sugeno-Kang fuzzy inference system (FIS). Then, an OLFESN-based control scheme is devised to implement the efficient control of redundant manipulators. Furthermore, simulations and experiments on redundant manipulators, covering UR5 and Franka Emika Panda manipulators, are carried out to verify the effectiveness of the proposed control scheme.

Infinite-dimensional reservoir computing.

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to a dynamic context. This new class is characterized by the readouts with a certain integral representation built on infinite-dimensional state-space systems. It is shown that this class is very rich and possesses useful features and universal approximation properties. The reservoir architectures used for the approximation and estimation of elements in the new class are randomly generated echo state networks with either linear or ReLU activation functions. Their readouts are built using randomly generated neural networks in which only the output layer is trained (extreme learning machines or random feature neural networks). The results in the paper yield a recurrent neural network-based learning algorithm with provable convergence guarantees that do not suffer from the curse of dimensionality when learning input/output systems in the class of generalized Barron functionals and measuring the error in a mean-squared sense.

Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks.

The echo state network (ESN) is an excellent machine learning model for processing time-series data. This model, utilising the response of a recurrent neural network, called a reservoir, to input signals, achieves high training efficiency. Introducing time-history terms into the neuron model of the reservoir is known to improve the time-series prediction performance of ESN, yet the reasons for this improvement have not been quantitatively explained in terms of reservoir dynamics characteristics. Therefore, we hypothesised that the performance enhancement brought about by time-history terms could be explained by delay capacity, a recently proposed metric for assessing the memory performance of reservoirs. To test this hypothesis, we conducted comparative experiments using ESN models with time-history terms, namely leaky integrator ESNs (LI-ESN) and chaotic echo state networks (ChESN). The results suggest that compared with ESNs without time-history terms, the reservoir dynamics of LI-ESN and ChESN can maintain diversity and stability while possessing higher delay capacity, leading to their superior performance. Explaining ESN performance through dynamical metrics are crucial for evaluating the numerous ESN architectures recently proposed from a general perspective and for the development of more sophisticated architectures, and this study contributes to such efforts.

Fast and adaptive dynamics-on-graphs to dynamics-of-graphs translation.

Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the "dynamics on graphs" (e.g., changing node attribute values) are visible, and they may be connected to and suggestive of the "dynamics of graphs" (e.g., evolution of the graph topology). Due to two fundamental obstacles, modeling and mapping between them have not been thoroughly explored: (1) the difficulty of developing a highly adaptable model without solid hypotheses and (2) the ineffectiveness and slowness of processing data with varying granularity. To solve these issues, we offer a novel scalable deep echo-state graph dynamics encoder for networks with significant temporal duration and dimensions. A novel neural architecture search (NAS) technique is then proposed and tailored for the deep echo-state encoder to ensure strong learnability. Extensive experiments on synthetic and actual application data illustrate the proposed method's exceptional effectiveness and efficiency.

Case Report: Low-grade endometrial stromal sarcoma with intravenous and intracardiac extension: a case after misdiagnosis of endometrial stromal nodule as submucosal fibroid.

We present herein a rare case of large vascular and cardiac metastases of low-grade endometrial stromal sarcoma (LG-ESS) in a female patient, which occurred after misdiagnosis of endometrial stromal nodule (ESN) as submucosal leiomyoma 7 years ago. Preoperative three-dimensional CT reconstruction was used to assess the extent of the lesion. The patient underwent radical resection: thrombectomy and total hysterectomy with bilateral salpingo-oophorectomy without establishing the cardiopulmonary bypass. Intraoperative transesophageal ultrasound (TEE) was used to monitor whether the intracardiac mass was removed completely. To date, this patient is alive without any evidence of recurrence 3 years after surgery. The differential diagnosis of ESN and LG-ESS is often difficult. A clear distinction can only be reliably made after histological analysis of the tumor's entire interface with the neighboring myometrium. This case highlights that follow-ups of patients with ESN are important. Regular follow-up can detect metastasis and recurrence of misdiagnosed LG-ESS as early as possible. Distant metastasis of LG-ESS is rare, especially involving large vessels or the heart. The treatment should largely rely on multidisciplinary cooperation. Although the surgery is traumatic, the perioperative mortality rate is low, and patients can avoid death from congestive heart failure or sudden death.

Utilizing variable auto encoder-based TDO optimization algorithm for predicting loneliness from electrocardiogram signals.

Several seniors and a substantial part of the general population are living in social isolation. This frequently occurs in vulnerability, isolation, and depression, which then have a poor impact on other health-related factors. A number of health problems, including a higher risk of cardio problems, are brought on by social isolation and loneliness. Electrocardiogram (ECG) usage for mental condition recognition enables accurate determination of a person's internal representation. The electrocardiogram (ECG) signals can be thoroughly analyzed to uncover hidden data that may be helpful for the precise identification of cardiac problems. ECG time-series information typically have great dimensions and complicated componentry. Using relevant information to guide training is among the main achievements of this type of learning. An ECG signal plays a significant part in the individual body's ability to manage behavior. Furthermore, loneliness identification is crucial since it has the worse effect on the circumstances that afflict persons. This study suggested an approach for detecting loneliness from an ECG signal to use a variable auto encoder-based optimization algorithm for ESN technique. The suggested approach consists of three phases for identifying a person's loneliness. Firstly, undecimated discrete wavelet transform is used to preprocess the acquired ECG data. Next, further characteristics are extracted from the precompiled signals using a variable auto encoder. For the precise categorization of loneliness in the ECG signal, a metaheuristic optimized ESN is, therefore, presented. The outcomes of the tests demonstrate that the suggested system with suitable ECG representations produces improved accuracy as well as performance.

A new rainfall prediction model based on ICEEMDAN-WSD-BiLSTM and ESN.

Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash-Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development.

An IoT Enable Anomaly Detection System for Smart City Surveillance.

Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods.

Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks.

The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time.

Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data.

Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA's tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters.

AQI time series prediction based on a hybrid data decomposition and echo state networks.

A hybrid AQI time series prediction model is proposed based on EWT-SE-VMD secondary decomposition, ICA (imperialist competitive algorithm) feature selection, and ESN (echo state network) neural network. Firstly, EWT (empirical wavelet transform) and VMD (variational mode decomposition) are used to decompose the original AQI time series into several stable and reliable subseries. Then, the ICA is used to select features of the above subseries for the ESN prediction model. Finally, the optimized feature variables are put into the ESN deep network to establish a prediction model of each AQI subseries and obtain the future AQI index. According to the experimental results of the daily AQI series in Beijing, Tianjin, and Shijiazhuang, we find that (a) among all decomposition methods, the proposed secondary decomposition method (EWT-SE-VMD) performs best in processing data; (b) it is proved that the proposed hybrid model has broad application prospect and research value in the AQI prediction field.

Effects of neurokinin 3 receptor antagonist fezolinetant on hot flash-like symptoms in ovariectomized rats.

The majority of women experience vasomotor symptoms (VMS), such as hot flashes and night sweats, during the menopausal transition. Recent evidence strongly suggests a connection between neurokinin 3 (NK3) receptor signaling and VMS associated with menopause. The NK3 receptor antagonist fezolinetant is currently in phase 3 development for treatment of moderate to severe VMS associated with menopause. We investigated the pharmacological effects of repeated administration of fezolinetant on levels of sex hormones and gonadotropins, neuronal activity in the hypothalamus, and skin temperature as an index of hot flash-like symptoms in ovariectomized rats as a model of menopause. Ovariectomized rats exhibited several typical menopausal symptoms: hyperphagia, increased body weight, significantly decreased plasma estradiol levels, increased luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels, and significantly increased skin temperature. Increased c-Fos expression (an indirect marker of neuronal activity) in median preoptic nucleus (MnPO) hypothalamic neurons was also observed in ovariectomized rats. Repeated oral administration of fezolinetant (1-10 mg/kg, twice daily) for 1 week dose-dependently reduced plasma LH levels without affecting estradiol or FSH levels, inhibited the activation of MnPO neurons, and attenuated hot flash-like symptoms. In addition, fezolinetant dose-dependently reduced hyperphagia and weight gain in ovariectomized rats. These preclinical findings suggest that fezolinetant attenuates hot flash-like symptoms via inhibition of neuronal activity in the MnPO of ovariectomized rats and provides further support for the ongoing clinical development of fezolinetant for the treatment of VMS associated with menopause.

Update on Endometrial Stromal Tumours of the Uterus.

Endometrial stromal tumours (ESTs) are rare, intriguing uterine mesenchymal neoplasms with variegated histopathological, immunohistochemical and molecular characteristics. Morphologically, ESTs resemble endometrial stromal cells in the proliferative phase of the menstrual cycle. In 1966 Norris and Taylor classified ESTs into benign and malignant categories according to the mitotic count. In the most recent classification by the WHO (2020), ESTs have been divided into four categories: Endometrial Stromal Nodules (ESNs), Low-Grade Endometrial Stromal Sarcomas (LG-ESSs), High-Grade Endometrial Stromal Sarcomas (HG-ESSs) and Undifferentiated Uterine Sarcomas (UUSs). ESNs are clinically benign. LG-ESSs are tumours of low malignant potential, often with indolent clinical behaviour, with some cases presented with a late recurrence after hysterectomy. HG-ESSs are tumours of high malignant potential with more aggressive clinical outcome. UUSs show high-grade morphological features with very aggressive clinical behavior. With the advent of molecular techniques, the morphological classification of ESTs can be integrated with molecular findings in enhanced classification of these tumours. In the future, the morphological and immunohistochemical features correlated with molecular categorisation of ESTs, will become a robust means to plan therapeutic decisions, especially in recurrences and metastatic disease. In this review, we summarise the morphological, immunohistochemical and molecular features of ESTs with particular reference to the most recent molecular findings.

Using ESN-Smartphone Application to Maximize AIS Reperfusion Therapy in Alexandria Stroke Network: A Stroke Chain of Survival Organizational Model.

Background: In developing countries like Egypt, the clinical workflow of stroke management is poorly established due to the lack of awareness of the stroke patients concerning their need of therapeutic intervention and the poor identification of facilities equipped to treat stroke. Hence, establishing a stroke system of care in developing countries that can efficiently and rapidly triage patients to the appropriate reperfusion therapy center is imperative to improving stroke management and outcomes. Aims: To evaluate a pilot experience in stroke hospital identification and expediting decision-making in AIS treatment through the Alexandria stroke network and Egyptian Stroke Network (ESN)-app. Methods: Between 2017 and 2019, seven hospitals registered themselves on the AS-Network as pilot hospitals. The ESN-application was used to detect stroke type, tele-connect stroke teams and hospitals, track triage of patients to equipped facility in real time, and streamline stroke workflow. The quality of and time required for stroke management were compared between 84 patients with acute ischemic stroke (AIS) whose treatment involved the ESN-app and 276 patients whose treatment did not. Results: During this pilot study, 360 AIS cases received reperfusion therapy, 84 of which were indicated by the ESN-app. The use of the application was associated with the significant drop in time metrics for the reperfusion AIS-patients (door-in-door-out time; 56 ± 34 min vs. 96 ± 45 min, door-to-groin puncture time; 50 ± 7 min vs. 120 ± 25 min, door-to-needle time; 55 ± 12 min vs. 78 ± 16 min with p < 0.0001). Its use was also associated with higher rates of excellent outcomes at the 90-day follow-up (without ESN-app vs. with ESN-app, 67.9 vs. 47.1%, p = 0.001) but no difference in 90-day mortality or symptomatic intracerebral hemorrhage (without ESN-app vs. with ESN-app, 9.5 vs. 11.2% and 4.8 vs. 5.1%, p > 0.05). Conclusion: Our pilot experience demonstrated that the use of the ESN-app expedited the stroke treatment workflow and facilitated tele-connection between registered stroke facilities. Additionally, its use might be associated with achieving higher rates of excellent outcomes at 90 days, where a larger scale study is needed for more confirmation.

Mediational Role of Self-efficacy between Selected Bio-psychosocial Factors and Elder Self-Neglect among Community-Dwelling Older Malaysian Adults.

Elder abuse and neglect have been reported as significant public health and societal problem in many different societies across the world. In Malaysia, older adults recorded a high prevalence of neglect compared to other types of elder abuse. There is a dearth of empirical work on elder self-neglect (ESN) among the older population in Malaysia. This paper aims to explore the mediational role of self-efficacy on the relationship between selected biopsychosocial factors and ESN among community-living older adults in Selangor, Malaysia. This study utilized a cross-sectional survey to gather data from a representative sample of 202 older adults from Selangor. A newly developed scale of 16-items of elder self-neglect (ESN) was used in this study. The results showed that self-efficacy partially mediated the association between ADL, IADL, depression, and capacity of self-care on ESN. Self-efficacy also fully mediated the association between neuroticism, life satisfaction, social network, and education on ESN. These findings provided a deeper understanding of the phenomenon of self-neglect among older Malaysian adults. The results will also serve as a useful reference for professionals and policymakers to develop uniform guidelines, protocols, or programs to handle cases of elder self-neglect in the community.

A Soft Sensor Approach Based on an Echo State Network Optimized by Improved Genetic Algorithm.

In the process of fault diagnosis and the health and safety operation evaluation of modern industrial processes, it is crucial to measure important state variables, which cannot be directly detected due to limitations of economy, technology, environment and space. Therefore, this paper proposes a data-driven soft sensor approach based on an echo state network (ESN) optimized by an improved genetic algorithm (IGA). Firstly, with an ESN, a data-driven model (DDM) between secondary variables and dominant variables is established. Secondly, in order to improve the prediction performance, the IGA is utilized to optimize the parameters of the ESN. Then, the immigration strategy is introduced and the crossover and mutation operators are changed adaptively to improve the convergence speed of the algorithm and address the problem that the algorithm falls into the local optimum. Finally, a soft sensor model of an ESN optimized by an IGA is established (IGA-ESN), and the advantages and performance of the proposed method are verified by estimating the alumina concentration in an aluminum reduction cell. The experimental results illustrated that the proposed method is efficient, and the error was significantly reduced compared with the traditional algorithm.

In-Ear EEG Based Attention State Classification Using Echo State Network.

It is important to maintain attention when carrying out significant daily-life tasks that require high levels of safety and efficiency. Since degradation of attention can sometimes have dire consequences, various brain activity measurement devices such as electroencephalography (EEG) systems have been used to monitor attention states in individuals. However, conventional EEG instruments have limited utility in daily life because they are uncomfortable to wear. Thus, this study was designed to investigate the possibility of discriminating between the attentive and resting states using in-ear EEG signals for potential application via portable, convenient earphone-shaped EEG instruments. We recorded both on-scalp and in-ear EEG signals from 6 subjects in a state of attentiveness during the performance of a visual vigilance task. We have designed and developed in-ear EEG electrodes customized by modelling both the left and right ear canals of the subjects. We use an echo state network (ESN), a powerful type of machine learning algorithm, to discriminate attention states on the basis of in-ear EEGs. We have found that the maximum average accuracy of the ESN method in discriminating between attentive and resting states is approximately 81.16% with optimal network parameters. This study suggests that portable in-ear EEG devices and an ESN can be used to monitor attention states during significant tasks to enhance safety and efficiency.

A novel combined model based on echo state network - a case study of PM10 and PM2.5 prediction in China.

Particulate Matters such as PM10, PM2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural network algorithm which based on Elman, echo state network (ESN) and cascaded BP neural network (CBP) to predict PM10 and PM2.5. In order to further improve the performance of the prediction result, we use the simulated annealing algorithm (SA) to optimize the parameters in the combination method to form the optimal combination model. And particle swarm optimization (PSO) is used to optimize the parameters in ESN. The chemical species in the atmosphere which include SO2, NO, NO2, O3 and CO in Baiyin, Gansu Province of China are used to test and verify the proposed combined method. The experimental results show that the prediction performance of the combined model presented in this paper is indeed superior to other three neural network models.

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review.

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons-an aspect that is under-explored in the literature.