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Sensor scheduling for energy-efficient collaborative target tracking in wireless sensor networks (WSNs) is an important problem to deal with the limited network resources. With the recent development and emerging applications of energy acquisition technologies, it has become possible to overcome the bottleneck of battery energy in WSNs using the energy harvesting devices, where theoretically the lifetime of the network could be extended to the infinite. However, the energy harvesting WSN also poses new challenges for sensor scheduling algorithm over the infinite horizon under the limited sensor energy harvesting capabilities. In this article, a novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking in energy harvesting WSNs to schedule sensors over an infinite horizon, according to the ADP mechanism. The “action” module of MSPADP is designed to obtain the sensor scheduling for multiple steps starting from the current step, and implemented by the minimal-cost first search (MCFS) decision tree scheme, and the “critic network” module of MSPADP is iteratively performed to optimize the performance for the remaining infinite steps using neural network. Extended Kalman filter (EKF) is adopted to predict and estimate the target state. The performance index is defined by the tracking accuracy derived from EKF and the energy consumption predicted by the candidate sensor schedule. Theoretical analysis shows the optimality of MSPADP, and simulation results demonstrate its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) sensor scheduling approaches. Note to Practitioners —Collaborative target tracking is a typical problem in wireless sensor networks (WSNs) where the sensors need to be scheduled to address the constraints of the limited network resources, such as sensor energy usually supplied by the battery. In the recent years, energy harvesting device has been developed and applied to WSNs to overcome the energy restriction. As the energy harvesting capabilities of the sensors are limited, sensor scheduling remains as a challenging problem and is studied in this article. A novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking, by scheduling sensors for the current time step based on the predictions of the subsequent steps over an infinite horizon. It runs iteratively in two modules: obtaining the previous optimal multistep sensor scheduling and updating the remaining infinite-step performance. Simulation results show its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) approaches, and lay a good foundation for the practical applications.

Ubiquitous WiFi signals not only provide fundamental communications for a large number of Internet of Things devices, but also enable to estimate target’s location in a contactless manner. However, most of the existing device-free localization (DFL) methods only utilize the time dynamics of the received WiFi signals, leading to inaccurate DFL in the cluttered indoor environments. Because different layouts of environments and deployments of WiFi devices cause the different mathematical distributions of the data collected from the cluttered indoor environments. In this article, a multiple kernel representation-based extreme learning machine (ELM) is proposed, named integrated multiple kernel ELM (IMK-ELM), for strengthening the localization performance in the cluttered indoor environments utilizing spatiotemporal information. In the proposed IMK-ELM-based DFL, the whole data set is first divided into several subsets depending on their mathematical distributions through the $K$ -means clustering algorithm, and then a corresponding number of local DFL models are built for all the subsets to capture both the time dynamics and spatial properties of the data. Finally, a global DFL model is achieved by seamlessly integrating all the local DFL models due to the consistency mechanism. In addition, the Fresnel zone sensing theory is utilized for helping understand and explain the essence of indoor DFL. Comprehensive experiments indicate that the proposed IMK-ELM-based DFL outperforms state-of-the-art methods in the cluttered indoor environments.

The main objective of this paper is to handle the trajectory tracking control for a class of three-wheel mobile robot by using a novel adaptive robust control. The mobile robot consists of nonholonomic constraints and uncertainties. The uncertainties include initial condition offset, mass, and moment of inertia of the system, which are time-varying and bounded (unknown). The control force in analytical form is obtained by UK theory, and the adaptive robust control is formulated to handle the uncertainties. For estimating the unknown bounds of uncertainties, the leakage-type adaptive law is designed, which can automatically adjust its own value according to the trajectory tracking errors. Then we verify the uniform boundedness and uniform ultimate boundedness of the proposed control by Lyapunov method. Finally, numerical simulations are conducted to show the trajectory tracking effectiveness of the designed control method.

Device-free localization (DFL) is becoming one of the attractive techniques in wireless sensing field, due to its advantage that the target does not need to be attached to any electronic device. However, most of the existed approaches for DFL can only obtain satisfactory localization performance in specific small area, they cannot function well when implemented to complex and large area. In order to tackle this issue, in this article, a hierarchical framework is developed for large-scale DFL based on data and knowledge twin driven integration, which consists of two phases, including the offline training phase and the online localization phase. In the offline training phase, the complex and large monitoring area is first divided into some subdomains by the K-means clustering algorithm, and then training corresponding number of broad learning (BL)-based DFL models for each subdomain using the Fresnel phase difference as the fingerprints. Meanwhile, a class-specific cost regulation extreme learning machine (CCR-ELM) classifier is also trained for determining the attribution of the reference points, which can alleviate the impacts of imbalanced data distribution on classification results. In the online localization phase, the attributions of the testing points are first judged through the trained CCR-ELM classifier, after that, estimating the target’s location in the corresponding subdomains using BL-based DFL models. The validity of the proposed hierarchical framework is evaluated both in small and larger areas, respectively.

Current methods based on the traditional i-vectors and deep neural network (DNN) have shown effectiveness on the speaker identification task, especially with the corpus of large scale. However, when the size of the training dataset is small, the overfitting problem may happen and lead to performance degradation. Besides, the robust identification still remains a challenging problem even under the less strict requirements. This paper proposes a novel approach, SpeakerGAN, for speaker identification with the conditional generative adversarial network (CGAN). It allows the adversarial networks for distinguishing real/fake samples and predicting class labels simultaneously. We configure the generator and the discriminator in SpeakerGAN with the gated convolutional neural network (CNN) and the modified residual network (ResNet) to obtain generated samples of high diversity as well as increase the network capacity. The multiple loss functions are combined and optimized to encourage the correct mapping and accelerate the convergence. Experimental results show that SpeakerGAN reduces the classification error rate by 87% and 16% compared with the traditional i-vector system and the state-of-the-art DNN based method. Under the scenario of limited training data, SpeakerGAN obtains significant improvement over the baselines. In the case of taking 1.6 s of each speaker for testing, SpeakerGAN achieves the identification accuracy of 98.20%, which suggests the promise for short-utterance speaker identification.

Imbalanced classification is a challenging task in the fields of machine learning and data mining. Cost-sensitive learning can tackle this issue by considering different misclassification costs of classes. Weighted extreme learning machine (W-ELM) takes a cost-sensitive strategy to alleviate the learning bias towards the majority class to achieve better classification performance. However, W-ELM may not achieve the optimal weights for the samples from different classes due to the adoption of empirical costs. In order to solve this issue, multi-objective optimization-based adaptive class-specific cost extreme learning machine (MOAC-ELM) is presented in this paper. To be Specific, the initial weights are first assigned depending on the class information. Based on that, the representation of the minority class could be enhanced by adding penalty factors. In addition, a multi-objective optimization with respect to penalty factors is formulated to automatically determine the class-specific costs, in which multiple performance criteria are constructed by comprehensively considering the misclassification rate and generalization gap. Finally, ensemble strategy is implemented to make decisions after optimization. Accordingly, the proposed MOAC-ELM is an adaptive method with good robustness and generalization performance for imbalanced classification problems. Comprehensive experiments have been performed on several benchmark datasets and a real-world application dataset. The statistical results demonstrate that MOAC-ELM can achieve competitive results on classification performance.

Hot metal silicon content is an important indicator for measuring the smooth operation of the blast furnace. However, the hot metal silicon content cannot be directly detected online. Hence, this paper proposes a prediction model of the hot metal silicon content based on the improved multi-layer online extreme learning machine (ML-OSELM). The improved ML-OSLEM algorithm is based on ML-OSELM, the variable forgetting factor (VFF) and the ensemble model. VFF is introduced to make the new coming data get more emphasis. The ensemble model can overcome the overfitting problem of ML-OSELM. This improved algorithm is named as EVFF-ML-OSELM. The real blast furnace production data are used to testify the established prediction model based on EVFF-ML-OSELM. Compared with the prediction models of the hot metal silicon content based on other algorithms, the simulation results demonstrate that the prediction model based on EVFF-ML-OSELM has better prediction accuracy and generalization performance.

Extreme learning machine (ELM) is an emerging machine learning algorithm for training single-hidden-layer feedforward networks (SLFNs). The salient features of ELM are that its hidden layer parameters can be generated randomly, and only the corresponding output weights are determined analytically through the least-square manner, so it is easier to be implemented with faster learning speed and better generalization performance. As the online version of ELM, online sequential ELM (OS-ELM) can deal with the sequentially coming data one by one or chunk by chunk with fixed or varying chunk size. However, OS-ELM cannot function well in dealing with dynamic modeling problems due to the data saturation problem. In order to tackle this issue, in this paper, we propose a novel OS-ELM, named adaptive OS-ELM (AOS-ELM), for enhancing the generalization performance and dynamic tracking capability of OS-ELM for modeling problems in nonstationary environments. The proposed AOS-ELM can efficiently reduce the negative effects of the data saturation problem, in which approximate linear dependence (ALD) and a modified hybrid forgetting mechanism (HFM) are adopted to filter the useless new data and alleviate the impacts of the outdated data, respectively. The performance of AOS-ELM is verified using selected benchmark datasets and a real-world application, i.e., device-free localization (DFL), by comparing it with classic ELM, OS-ELM, FOS-ELM, and DU-OS-ELM. Experimental results demonstrate that AOS-ELM can achieve better performance.

Extreme learning machine (ELM) is an emerging machine learning technique for training single hidden layer feedforward networks (SLFNs). During the training phase, ELM model can be created by simultaneously minimizing the modeling errors and norm of the output weights. Usually, squared loss is widely utilized in the objective function of ELMs, which is theoretically optimal for the Gaussian error distribution. However, in practice, data collected from uncertain and heterogeneous environments trivially result in unknown noise, which may be very complex and cannot be described well using any single distribution. In order to tackle this issue, in this paper, a robust ELM (R-ELM) is proposed for improving the modeling capability and robustness with Gaussian and non-Gaussian noise. In R-ELM, a modified objective function is constructed to fit the noise using mixture of Gaussian (MoG) to approximate any continuous distribution. In addition, the corresponding solution for the new objective function is developed based on expectation maximization (EM) algorithm. Comprehensive experiments, both on selected benchmark datasets and real world applications, demonstrate that the proposed R-ELM has better robustness and generalization performance than state-of-the-art machine learning approaches.

In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in an energy-efficient state. In the proposed scheme, production indicators prediction models are first developed using a kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multiobjective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of the approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which an improved association rule mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

Collaborative filtering (CF) is the most classical method for recommender system, but it is usually suffered from limited performance by the sparseness of user-to-item rating data. Recently, due to the powerful learning feature representation ability, deep learning components are used to leverage auxiliary information to assist recommendation. However, most existing models based on deep learning are incomplete so that merely extracting the item latent representation and ignoring the user parts. Besides, different data are not chosen from current models. This paper proposes a novel probability framework, named as joint matrix factorization (JMF). There are three components in JMF. Firstly, the modified multilayer crossing version of the factorization machine (MFM) is designed to extract the user latent factors based on user behavior information. Moreover, MFM is a general method which can be used to accomplish many tasks in terms of machine learning. Secondly, a modification of Long Short-Term Memory (LSTM), named as bidirectional LSTM (BLSTM), is used to extract the item latent factors of a document sequence from both front and back directions. Finally, we tightly integrate BLSTM and MFM into probabilistic matrix factorization (PMF) to form JMF. Compared with the classical matrix factorization and other integration models, JMF extracts document data as well as user behavioral data as item vectors and user vectors. Extensive experiments on five real-world datasets show the proposed model has better performance compared with the state-of-the-art recommendation methods.

Accurate sensing and understanding of gestures can improve the quality of human-computer interaction, and has great theoretical significance and application potentials in the fields of smart home, assisted medical care, and virtual reality. Device-free wireless gesture recognition based on WiFi Channel State Information (CSI) requires no sensors, and has a series of advantages such as permission for non-line-of-sight scenario, low cost, preserving for personal privacy and working in the dark night. Although most of the current gesture recognition approaches based on WiFi CSI have achieved good performance, they are difficult to adapt to the new domains. Therefore, this paper proposes ML-WiGR, an approach for device-free gesture recognition in cross-domain applications. ML-WiGR applies convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks as the basic model for gesture recognition to extract spatial and temporal features. Combined with the meta learning training mechanism, the approach dynamically adjusts the learning rate and meta learning rate in training process adaptively, and optimizes the initial parameters of a basic model for gesture recognition, only using a few samples and several iterations to adapt to new domain. In the experiments, we validate the approach under a variety of scenarios. The results show that ML-WiGR can achieve comparable performance against existing approaches with only a small number of samples for training in cross domains.

The advance of theory and techniques result in the emergence of various Brain Training Platforms. Based on researching results from psychology and cognitive neuron science, these platforms are helping their users to monitor and improve their brain capacities, and have received attentions and expectations from the public. By analyzing representative platforms and review related researches, we found that these platforms mostly use cognitive games adapted from classic cognitive paradigms to train users in a closed-loop manner. However, their validity and effective are found obscure. Basing on this analysis, we further discuss the design and development of future platforms.

Numerous studies have found that previous information can affect the current explicit working memory processing. However, the study that investigate the effect of previous episodes on the implicit working memory processing has been limited. The present study investigated this issue in healthy older adults and patients with mild cognitive impairment (MCI). A hybrid delayed-match-to-sample task was used to examine the implicit working memory processing regardless of explicit memory components. Functional magnetic resonance imaging data was collected. The behavioral results demonstrated that the previously studied information was able to facilitate the following implicit working memory processing. Nevertheless, this effect was impaired in MCI patients, although both healthy older adults and MCI patients have shown significant implicit working memory processing. Whole-brain activation analysis revealed that there were significant interactions between group and previous information (Studied and Non-studied) in the left cuneus. The psychophysiological-interaction analysis showed that the functional connectivity between left cuneus and regions in bilateral middle occipital gyrus as well as dorsal regions of left precuneus was modulated by previous episodic information in healthy older adults. However, no significant region was found in MCI patients. The current study for the first time revealed that the previous episodic information can facilitate the following implicit working memory procession in healthy older adults, and demonstrated the key cortical structure and cortical pathways underlying the interactions between previous episodic information and current implicit working memory processing.

In this paper, trajectory tracking control is investigated for lower extremity rehabilitation exoskeleton robot. Unknown perturbations are considered in the system which are inevitable in the reality. The trajectory tracking control is constructively treated as constrained control issue. To obtain the explicit equation of motion and analytical solution of lower extremity rehabilitation exoskeleton robot, Udwadia-Kalaba theory is introduced. Lagrange multipliers and pseudo variables are not needed in Udwadia-Kalaba theory, which is more superior than Lagrange method. On the basic of Udwadia-Kalaba theory, two constrained control methods including trajectory stabilization control and adaptive robust control are proposed. Trajectory stabilization control applies Lyapunov stability theory to modify the desired trajectory constraint equations. A leakage-type of adaptive law is designed to compensate unknown perturbations in adaptive robust control. Finally, comparing with nominal control and control method in [32], simulation results demonstrate the superiority of trajectory stabilization control and adaptive robust control in trajectory tracking control.

Vital signs such as heartbeat and respiration signals are significant indicators for health care and clinical applications. Non-contact vital signs detection via mm-wave radar has attracted more attention due to more comfortable experience and lower burden. However, the non-contact heartbeat and respiration signals detection with random body movements is more challenging. In this paper, we propose a general framework to address this problem. It is termed DRSEPK and consists of signal decomposition and reconstruction, spectrum estimation and spectral peak tracking. Signal decomposition and reconstruction is applied for denoising and reconstructing cleaned signal. Spectrum estimation aims to get high-resolution frequency spectrum. The spectral peak tracking can select correct spectral peaks corresponding to breath rate (BR) and heartbeat rate (HR). Experiments are conducted using frequency modulated continuous wave (FMCW) radar on ten subjects who are typing on a laptop. The results show that the DRSEPK framework has high estimation accuracy and is reliable for non-contact vital signs detection during random body movements.

Burden distribution is an important method to regulate the operation state of blast furnace. Reasonable burden distribution matrix of blast furnace is the prerequisite for improving resource utilization and ensuring stable blast furnace condition. In order to achieve the above target, an ensemble prediction model based on weighted voting and a method of calculating weights are proposed in this paper. In this model, the convolution neural network (CNN) - bidirectional gated recurrent unit (BiGRU) based on attention mechanism, logistic regression, k-nearest neighbor (KNN) and support vector machines (SVM) are integrated to predict whether the burden distribution matrix of blast furnace needs to be adjusted. The weight of each individual model is determined by its prediction accuracy and F1-score. CNN converts the extracted feature vectors into time series and inputs them into BiGRU. The attention mechanism is introduced to assign different weights to the hidden state of BiGRU, so as to enhance the long-term influence of important information. Finally, the production data of blast furnace are used to testify the performance of prediction models. In this paper, Multilayer Perceptron (MLP) model, RNN model, BiGRU model, BiGRU model based on attention mechanism, logistic regression model, KNN model, SVM model and the proposed ensemble model are used for comparison experiments, compared with other prediction models, the experimental results illustrated that the weighted voting ensemble prediction model can accurately determine whether the burden distribution matrix of blast furnace needs to be adjusted and have better prediction accuracy and generalization performance.

The whale optimization algorithm(WOA) is a novel meta-heuristic evolutionary algorithm inspired by the behavior of whales predation. An important factor to the success of WOA is the balancing between exploration and exploitation. In the WOA, the distance control parameter $a$ is the main factor to find an appropriate balance between exploration and exploitation. In the standard WOA, the distance control parameter $a$ is optimized by linear control strategy (LCS), but the process of whales predation is not simply linear process. To address the issue, this paper proposed a nonlinear control strategy based on arcsine function (NCS-Arcsin) to optimize WOA. The NCS-Arcsin is applied to adjust distance control parameter $a$ . The NCS-Arcsin is considered to accurately describe the process of whales predation. Experiments on twelve well-known benchmark functions show the NCS-Arcsin can significantly improve the exploration and exploitation capabilities of WOA. In addition, the performance of proposed NCS-Arcsin is compared with LCS and other have been proposed NCS. The experimental results show that the optimization effect of NCS-Arcsin is stronger than that of LCS and other NCS. The NCSs based on the arcsine function is the best NCS, which can significantly improve the optimize performance of WOA.

As the prodromal stage of Alzheimer's disease, effective recognition of mild cognitive impairment can reduce the prevalence of Alzheimer's disease. At present, most of the research on the recognition of mild cognitive impairment is carried out through biomarkers and neuroimaging, which is not conducive to large-scale analysis and research. Based on neuropsychological evaluation and life habits questionnaires, this article applies machine learning methods to the recognition of mild cognitive impairment and conducts experimental research. A questionnaire survey of the elderly was conducted to obtain raw data, including demographic variables, daily habits and neuropsychological data. Feature selection is carried out through filter screening method and the influence of factors such as lifestyle, physical health and learning ability on the morbidity of mild cognitive impairment is analyzed. The classifier mainly uses three methods: artificial neural network, support vector machine and random forest. The experimental results show that the random forest classification effect is the best and the accuracy rate is as high as 92%. Artificial neural network has strong generalization ability with 91% accuracy rate. Support vector machine has inferior effect.

In recent years, WiFi CSI based device-free localization has attracted widespread attention. Most of the previous work used Received Signal Strength Indication (RSSI), and the acquisition of Channel State Information (CSI) significantly improved the localization accuracy based on WiFi. In this paper, we propose a device-free indoor localization method based on CSI. First, we use CSI Tools to collect CSI and use some specific filters to filter out the noise. Then, we use Dynamic Time Warping (DTW) to extract the differences between the two data segments as fingerprints. Finally, Extreme Learning Machine (ELM) is used for modelling and calculating the location of the target. We set up a system in the laboratory to verify the method's performance, which confirmed the effectiveness of the method proposed in this paper.

Hot metal silicon content (HMSC) is usually utilized to measure the quality of hot metal and reflect the thermal status of blast furnace (BF) system. However, most state-of-the-arts ignore the time-varying behavior of BF ironmaking process, which are impractical. Accordingly, a novel dual ensemble online sequential extreme learning machine (DE-OS-ELM) is proposed to establish the online estimation model of HMSC, which can update the data-driven model with the latest operation data. Specifically, an online learning method with recursive modification is first proposed based on OS-ELM (referred to as RM-OS-ELM) to address the modeling with uncertainty. To heel, a dynamic forgetting factor is presented for the dynamic tracking capability enhancement and convergence acceleration. Furthermore, a final updating rule for sequential implementation is constructed by combining the output weights of OS-ELM and RM-OS-ELM based on their corresponding contributions on modeling. Considering the modeling accuracy and curve trend consistency, multiobjective parameter optimization model is also implemented to achieve the satisfactory performance. By taking the proposed DE-OS-ELM, the estimation model of HMSC is established using industrial data. Comprehensive experiments demonstrate that DE-OS-ELM-based HMSC estimation model is more feasible and practical.

Using cameras to capture information about a person's gait for identification is a new biometric technology, At present, the recognition accuracy at the angle of 90 degree has reached a high level, but the videos collected in real life are often from other different angles, and the existing methods for multiperspective recognition are not perfect, with a number of problems such as long training time and low accuracy. In view of the above problems, characteristics of Gait Energy Image (GEI) and barycenter features of gait sequence were fused using Kernel canonical correlation analysis (KCCA) in this paper, and Extreme learning machine (ELM) is proposed for classification, and Multi-perspective identification method based on Kernel canonical correlation analysis is proposed. Experimental results show that the feature fusion method can significantly improve the accuracy of recognition from non-90° angles.

Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules: (1) feature extraction of heartbeat signals, including RR interval features in the time-domain and wavelet time-frequency features, and (2) heartbeat classification using ELM based on the extracted features. RR interval features are calculated to reflect the dynamic characteristics of heartbeat signals. Discrete wavelet transform (DWT) is used to decompose the heartbeat signals and extract the time-frequency features of the heartbeat signals along the timeline. ELM is a single-hidden layer feedforward neural network with the hidden layer parameters randomly generated in advance and the output layer parameters calculated optimally using the least-square algorithm directly using the training samples. ELM is used as the heartbeat classification algorithm due to its high accuracy training accuracy, fast training speed, and good generalization ability. Experimental testing is carried out using the public MIT-BIH arrhythmia dataset to perform a 16-class classification. Experimental results show that the proposed approach achieves a superior classification accuracy with fast training and recognition speeds, compared with existing classification algorithms.

Mild cognitive impairment (MCI) is an aging disease mainly caused by memory impairment after the occurrence of brain lesions. EEG analysis is an effective non-invasive method to recognize brain activity and MCI. Due to the highly non-stationary characteristics of EEG, it is a challenging task to extract features from EEG signals and further improve classification performance for MCI. In this paper, we will present a novel deep learning approach for MCI based on convolutional neural networks (CNN) using EEG signals, where the CNN is used for feature extraction from EEG signals in cognitive tasks, a softmax function is utilized as classifier and creatively the attention mechanism is applied to one of the convolution operations. Experimental results show that the CNN with attention mechanism has an average accuracy of 79.66% (validation) after the accuracy of the validation set has stabilized, which is significantly higher than that of traditional convolutional networks. Compared with the highest accuracy of 70.09% in the other four existing approaches, it shows an obvious advantage. The proposed approach can enrich the convolution features of EEG, improve the model fitting ability and generalization performance, and realize the classification of MCI effectively.

The development of wireless power transmission technology provides a new method to the energy constraint problem of sensor networks. Extensive of research has been done to optimize the charging sequence of mobile charger to improve the charging efficiency and to prolong the life cycle of wireless rechargeable sensor networks (WRSN). Compared with the traditional research, this paper allows the nodes in the network to stop working, that is, the node energy is exhausted, and then it can resume working after getting the energy replenishment. At the same time, based on the consideration of charging efficiency, the concept of charging waiting time is introduced, and finally a novel mobile energy replenishment problem is proposed, that is the joint mobile charging sequence and charging start time control (JMCSCSTC) problem. We first build the system models and formulate the problem, then design three algorithms to address this problem. Finally a large number of simulation experiments are carried out to learn the impact of waiting time on the charging performance. At the same time, we change the parameters to compare the impact on the performance index, and verify the feasibility of the algorithm.

Extreme Learning Machine (ELM) is greatly fast in its learning speed and has been widely applied to a variety of applications. However, ELM is usually considered as a shallow-structured model, which has merely one hidden layer, with its performance restricted in some complicated applications. To enhance the performance of ELM, in this paper, we propose a novel Multi-Level Cascading ELM (MLC-ELM) which is composed of multiple ELMs, with each in a level. Furthermore, there is an information flow between adjacent levels, which is the transformation of the previous level’s output, performed through a normalization operation and a scaling operation. The information flow is considered to be part of the input of the level following it. The output of the proposed MLC-ELM algorithm is the output of the final level. We conduct experiments in a Channel State Information (CSI) based Device-Free Localization (DFL) scenario to demonstrate the validity of MLC-ELM, with the results showing its effectiveness.

In the past decade, deep learning techniques have powered many aspects of our daily life, and drawn ever-increasing research interests. However, conventional deep learning approaches, such as deep belief network (DBN), restricted Boltzmann machine (RBM), and convolutional neural network (CNN), suffer from time-consuming training process due to fine-tuning of a large number of parameters and the complicated hierarchical structure. Furthermore, the above complication makes it difficult to theoretically analyze and prove the universal approximation of those conventional deep learning approaches. In order to tackle the issues, multilayer extreme learning machines (ML-ELM) were proposed, which accelerate the development of deep learning. Compared with conventional deep learning, ML-ELMs are non-iterative and fast due to the random feature mapping mechanism. In this paper, we perform a thorough review on the development of ML-ELMs, including stacked ELM autoencoder (ELM-AE), residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications. In addition, we also discuss the connection between random neural networks and conventional deep learning.