Jian Zhang's research while affiliated with Nanjing University of Information Science & Technology and other places
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Publications (22)
Remaining useful life (RUL) prediction of mechanical components is of high research value in the field of prognostics and health management (PHM). However, RUL prediction problems are completely challenging due to the complicacy of bearings’ operating environment. In this paper, we transform the vibration acceleration signal collected by sensors in...
The deep forest (DF) model is built using a multilayer ensemble of forest units through decision tree aggregation. DF presents characteristics of an easy-to-understand structure, is suitable for small sample data, and has become an important research direction in the field of deep learning. These attributes are particularly suitable for the modelin...
Key process parameters such as production qualities and environmental pollution indices are difficult to be measured online in complex industrial processes. High time and economic costs make only limited small sample data be obtained to build process models, while the deep neural network model requires massive training samples. Although the deep fo...
In this paper, we investigated collaboration energy efficiency with mobile edge computing (MEC) mechanism in internet of things (IoT), which is a challenge issue. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing energy balance. We deign the sensor selection scheme for colle...
In this paper, the target tracking problem is investigated with mobile edge computing (MEC) mechanism in internet of things (IoT), where the challenge of energy efficiency is a significant issue when the target tracking event is driven. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while g...
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Several difficult-to-measure production qualities or environment pollution indices of industrial process must be measured using offline laboratory instruments. Soft measurement method is often used to perform online prediction of such parameters. Only small-sample modeling data with high-dimensional input features can be obtained due to the limitat...
Energy efficiency is the major concern in hierarchical wireless sensor networks(WSNs), where the major energy consumption originates from radios for communication. Due to notable energy expenditure of long-range transmission for cluster members and data aggregation for Cluster Head(CH), saving and balancing energy consumption is a tricky challenge...
Prolonging the lifetime of wireless sensor networks (WSNs) is a crucial issue referring to energy conservation or balancing of data collection. In this paper, we propose an optimal algorithm for data collection using UGVs based on clustering by definition of rendezvous nodes (RNs). Due to the considerable large difference of long-distance transmiss...
Energy efficiency receives significant attention in wireless sensor networks. In this paper, a UGV is employed as an energy-efficient solution to prolong the network lifetime in target tracking. Data collection strategies for target tracking are investigated including the amount of data and the transmitted distances. For contributed data, the quant...
Increased storage capacity, computing and communications power, coupled with advances in wireless networking technology, bring a potential to enable new applications for vehicular ad hoc network (VANETs), in which a large number of roadside units (RSUs) are deployed to facilitate the service for drivers and passengers in vehicles. In this paper, we...
Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives...
Random neural networks (RNNs) prediction model is built with a specific randomized algorithm by employing a single hidden layer structure. Duo to input weights and biases are randomly assigned and output weights are analytically calculated, it is widely used in different applications. Most of RNNs-based soft measuring models assign the random param...
The decorrelated neural network ensemble (DNNE) algorithm can be used to construct an effective soft measuring model through an analytical solution comprising submodels of multiple randomized neural networks. However, DNNE exhibits one major shortcoming: the scope of the random input weights and biases is set to a default range of [−1, 1], which ca...
Most of the intrusion detection models (IDM) are constructed with off-line training data. Time-variance characteristic of the practical network system cannot be embodied in the off-line constructed IDM. On-line updating of the off-line IDM with the valued new samples is very necessary. In this paper, a new on-line instruction detection model based...
Energy conservation or energy balance in the process of data gathering has always been a crucial issue for prolonging the lifetime of wireless sensor networks (WSNs), especially on the premise of the introduction of mobile elements to cope with the phenomenon of hot spots or energy holes. In this paper, we propose optimal cluster-based mechanisms b...
Energy conservation or energy balance in the process of data gathering has always been a crucial issue for prolonging the lifetime of wireless sensor networks (WSNs), especially on the premise of the introduction of mobile elements to cope with the phenomenon of hot spots or energy holes. In this paper, we propose optimal cluster-based mechanisms b...
Collinear and nonlinear characteristics of modeling data have to be addressed for constructing effective soft measuring models. Latent variables (LVs)-based modeling approaches, such as kernel partial least squares (KPLS), can overcome these disadvantages in certain degree. Selective ensemble (SEN) modeling can improve generalization performance of...
Heavy key mechanical devices relate to production quality and quantity of complex industrial process directly. It is necessary to estimate some difficulty-to-measure process parameters inside these devices. Multi-component and non-stationary mechanical signals, such as vibration and acoustic ones, are always employed to model these process paramete...
Energy consumption has always been a challenging issue in wireless sensor networks (WSNs). In this paper, we consider the collaboration optimization problem for load balancing with mobility-assisted features. In particular, we present a cluster-based network structure, in which sensor nodes are partitioned into layers according to transmission radi...
Citations
... LSTM has marvelous characteristics in modeling nonlinear data. Methods for modeling small sample data [41][42][43] include SVM, partial least squares (PLS), RF, gradient boosting decision tree (GBTD), etc., for predicting DXN emission concentration [44]. The dataset for the CO emission prediction model is abundant, exhibiting strong coupling and nonlinearity. ...
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... LSTM has marvelous characteristics in modeling nonlinear data. Methods for modeling small sample data [41][42][43] include SVM, partial least squares (PLS), RF, gradient boosting decision tree (GBTD), etc., for predicting DXN emission concentration [44]. The dataset for the CO emission prediction model is abundant, exhibiting strong coupling and nonlinearity. ...
... In Tang et al. (2020), the features are not extracted with the help of a-priori knowledge but grouped into meaningful and related groups based on expert knowledge. Based on this, feature selection is proposed where expert knowledge is incorporated into selecting a proper threshold value for the feature selection to keep the relevant features and neglect redundant information. ...
... To further cut energy use and extend the lifespan of WSNs, the authors of [11] considered tackling the relay selection problem. As a result, the suggested study used the k-means method to create clusters within the network. ...
... Use of supervision information to accelerate the learning process and improving the performance of learning process is not a new idea, and has been used before in other domains (Mohammadi et al., 2018;Zhang et al., 2019b), and has been known as reward shaping, but based on best of our knowledge, this is the first time to use the supervision information to improve the performance of learning in WSNs. ...
... Here, μ t Bias , σ t Bias are respectively the mean and the standard deviation of bias at the t-th time instance and μ min Bias , σ min Bias are that for the minimum bias till the time instance t. Once a new hidden node is added to the layer, the associated parameters (b, U , V ) can be set by employing adaptive scope selection mechanism (Wang and Li 2017;Tang et al. 2017). Similarly, a hidden node is pruned from the hidden layer when we detect high variance, indicating model-overfit, and thus, requirement for reducing structural complexity. ...
... All proposed soft sensor modeling methods based on ensemble learning [18][19][20] realized online soft sensor of concrete CS. However, the structure of the concrete CS soft sensor model in the above research literature is complex, and the representation learning of features is not considered between modules, and there are problems such as low prediction accuracy of the concrete CS soft sensor value [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. ...
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... A suitable number of replicas are stored on each node as processing and computation power of every node is different [19]. Chen et al. [20] developed a cooperative replication scheme, weighted dynamic data replication policy, proposes a system in which data are replicated by categorizing it as either hot (currently in use) or cold (stale or currently unused) data by assigning a weight based on its access popularity. ...
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... Similarly, Wang et al. 20 used the normalization of working conditions to realize the timefrequency expression of the stator side current of the motor under condition of variable speed, and accurately locate the rotor fault characteristics. Regarding the multi-source signal fusion perception, Tang et al. 21 proposed a multi-layer selective ensemble algorithm to construct mechanical vibration and acoustic frequency spectra. ...
... A mobile wireless sensor network (MWSN) is nothing but a wireless sensor network (WSN) in which the sensor nodes are mobile [1]. MWSNs were smaller and a developing filed as a contradictory to their well-established predecessor [2]. MWSNs are much more versatile when compared with the static sensor networks as they can be executed in any case and manage with the very quick topology changes [3]. ...
Reference: journal3-scopus (1)
