Conventional classification systems struggle to represent the rapid advancement of technology and the increasing prevalence of interdisciplinary research, necessitating more dynamic and comprehensive approaches to organizing technological knowledge. This article presents a methodology for constructing a multilayered Field of Technology (FOT) ontology that explicitly separates a static layer and a dynamic layer. The static layer is grounded in the International Patent Classification (IPC) and enriched with Wikipedia, providing a stable foundation of core technological domains. For the dynamic layer, we derive technical concepts from English-language patent titles in the Google Patents Public Data using a custom NER model (PatentNER), and organize them through a hierarchical construction method that integrates density-based and hierarchical clustering. The resulting ontology comprises a multi-level hierarchy in which more than half of the dynamic concepts span multiple technical domains. To the best of our knowledge, this is the first study to construct such a large-scale, hierarchical FOT ontology directly from patent data, providing a scalable and adaptable resource for understanding the complex and interdisciplinary nature of modern technologies.

Due to various restrictions, some Internet of Things (IoT) sensing layers can only deploy a small number of sensor nodes for spatiotemporal low-resolution environmental information sensing, making the urgent issue of how to recover the spatiotemporal high-resolution sensing data (SHD). Existing methods mainly focus on the reconstruction problem of random data loss in densely deployed nodes, while continuous data loss in spatiotemporal low-resolution sensing data (SLD) can severely degrade their reconstruction performance. In this work, an acrlong SLRTC is proposed to avoid the impact of continuous data loss and further enhance the spatiotemporal correlation. The SLD is arranged in a third-order tensor, where horizontal and vertical directions are node location indexes, and tubal direction is the time index. To avoid continuous data loss and enhance the spatial correlation of data, each frontal slice of the tensor is divided into a group of overlapping patches and then concatenated into a third-order spatial structure tensor. To further ensure the stricter low-rank prior, the spatial structure tensors are divided into two groups and linearly mapped to a third-order tensor with Hankel structure to exploit the spatiotemporal correlation among the data, and then the two Hankel tensors are concatenated into a three-order tensor for exploiting inter-Hankel tensor temporal correlation. Experimental results on real and simulated IoT data show that the proposed method can reconstruct SHD with high accuracy and the acrlong NMAE is lower than 0.0172 and 0.0124, respectively, when only 12% of the data is observed.

As acquired seismic data is usually incomplete and noisy, simultaneous reconstruction and denoising is an extremely important step for the accurate interpretation of seismic data and subsequent processing. We propose a hybrid low-rank and sparsity constraint method with Hankel structure preservation to improve the performance of simultaneous reconstruction and denoising. The proposed method combines the advantages of high efficiency pertaining to sparsity-promoting transforms and the strong data adaptability of rank reduction methods. Meanwhile, a structure-preserving matrix is constructed to preserve the predefined Hankel structure of the twofold Hankel matrix to further improve the accuracy and efficiency of simultaneous reconstruction and denoising. Moreover, weighted nuclear norm minimization (WNNM) is introduced to adaptively assign weights to different singular values. Experimental results in both synthetic and field seismic data compared with other state-of-the-art methods demonstrate the superior performance of the proposed method.

The emergence of the heterogeneous Internet of Things (IoT) has realized the demand for multi-attribute data collection in response to the increasing demand for information in diverse applications. Sparse sampling has been used to reduce network energy consumption in order to extend the life of energy-constrained networks. Real-time multi-attribute data aggregation under the sparse sensing framework has become a research focus. Therefore, we proposed a sparse-sampling-based IoT data aggregation approach to reduce network energy consumption and enable real-time multi-attribute reconstruction. For data collection, a sparse sampling data collection approach is proposed that can successfully collect and transmit the multi-attribute data to the sink even while certain IoT sensor nodes are in the sleep mode. For data reconstruction, a real-time multi-attribute data reconstruction method based on subspace is proposed. The proposed method arranges multi-attribute data in a tensor form in order to further utilize the correlation of multi-attribute data. Subspaces representing the spatial distributions of the multi-attribute data can be obtained from the previously reconstructed data. Incorporating total variation constraint, the proposed method reconstructs the current time slot multi-attribute data with high precision in real time. The experimental results demonstrate the effectiveness of the proposed method in real-time multi-attribute data reconstruction.

Coherent seismic noise is usually difficult to attenuate due to the similar morphological patterns between noise and useful signals. To attenuate coherent noise, special preknowledge should be utilized in a state-of-the-art approach, which causes significant inconvenience. Here, we develop an automatic method to attenuate coherent noise based on the adaptive dictionary learning algorithm. The adaptive dictionary algorithm can learn the features of both signals and coherent noise and leave obvious morphological differences in the dictionary atoms. These differences in the dictionary atoms can be transformed into statistical differences, which can be measured and then used to distinguish between signal and noise atoms. We evaluate several statistical metrics in characterizing the dictionary atoms and their feasibilities in distinguishing between signal and noise atoms. We find that the kurtosis metric can best represent the differences between signal and noise atoms, and then we design a kurtosis-based filter to reject those high-kurtosis atoms and their corresponding coefficient vectors for suppressing the coherent noise. Synthetic and real data examples demonstrate the performance of the proposed algorithm.

Abstract This paper presents a simple, fast and efficacious system to promote the human action classification outcome using the depth action sequences. Firstly, the motion history images (MHIs) and static history images (SHIs) are created from the front (XOY), side (YOZ) and top (XOZ) projected scenes of each depth sequence in a 3D Euclidean space through engaging the 3D Motion Trail Model (3DMTM). Then, the Local Binary Patterns (LBPs) algorithm is operated on the MHIs and SHIs to learn motion and static hierarchical features to represent the action sequence. The motion and static hierarchical feature vectors are then fed into a classifier ensemble to classify action classes, where the ensemble comprises of two classifiers. Thus, each ensemble includes a pair of Kernel-based Extreme Learning Machine (KELM) or ${\mathrm{l}}_{\mathrm{2}}$-regularized Collaborative Representation Classifier (${\mathrm{l}}_{\mathrm{2}}$-CRC) or Multi-class Support Vector Machine. To extensively assess the framework, we perform experiments on a couple of standard available datasets such as MSR-Action3D, UTD-MHAD and DHA. Experimental consequences demonstrate that the proposed approach gains a state-of-the-art recognition performance in comparison with other available approaches. Several statistical measurements on recognition results also indicate that the method achieves superiority when the hierarchical features are adopted with the KELM ensemble. In addition, to ensure real-time processing capability of the algorithm, the running time of major components is investigated. Based on machine dependency of the running time, the computational complexity of the system is also shown and compared with other methods. Experimental results and evaluation of the computational time and complexity reflect real-time compatibility and feasibility of the proposed system.

Cloud segmentation is one of the hot tasks in the field of weather forecast, environmental monitoring, site selection for observatory, and other areas. In this letter, we proposed a new deep convolutional neural network architecture called CloudU-Net for daytime and nighttime cloud images’ segmentation. The net consists of dilated convolution, activation, batch normalization (BN), max pooling, upsampling, skip connection, and fully connected conditional random field (CRF) layers. The benefits of the net architecture are four aspects: First, the dilated convolution increases the receptive field of the filters to obtain more information of the context without increasing the extra amount of computation and the extra number of parameters. Second, the BN layer increases the speed of network training and prevents over-fitting. Third, the fully connected CRF optimizes the output of the front end of the architecture, and finally gets better segmentation results. Finally, the enhanced optimizer Lookahead improves the learning stability and speeds up model convergence. Compared with the current deep-learning-based state-of-the-art cloud images’ segmentation algorithms, the CloudU-Net demonstrates better segmentation performance for daytime and nighttime cloud images.

Environmental monitoring is an important application of wireless sensor networks (WSNs). However, due to the limited number of sensors, the global distribution of the sensed physical environmental parameter with a high resolution in the monitoring area cannot be accurately obtained. In this paper, a structured matrix completion based method is proposed to obtain the global distribution of selected environmental parameter with partial sensors. By arranging the data into an enhanced matrix exhibiting Hankel structure, the inherent correlation among data in the monitoring area can be further exploited to improve the accuracy of data estimation. Furthermore, an efficient algorithm based on alternating direction method of multipliers is described to solve the resulting problem. Experimental results demonstrate that the proposed method can estimate the global distribution of the environmental parameter and achieves better estimation accuracy compared with the existing methods.

The seismic energy is the most correlative along the structural direction, and thus, many traditional filtering methods can be optimally performed in a flattened gather. We introduce in detail a flattening operator for creating the flattened dimension, where a denoising operator can be applied subsequently. The flattening operator is created by deriving a plane-wave trace continuation relation following the plane-wave equation. We demonstrate that the plane-wave trace continuation can well preserve the strong amplitude variation existing in the seismic data. In order to obtain a reliable slope estimation in the presence of noise, a robust slope estimation approach is introduced to substitute the traditional method. The flattening operator can be combined with many state-of-the-art filtering methods to obtain superior performance. Both synthetic and field seismic data are used to demonstrate the potential of the proposed framework in realistic applications.

Due to data loss and sparse sampling methods utilized in WSNs to reduce energy consumption, reconstructing the raw sensed data from partial data is an indispensable operation. In this paper, a real-time data recovery method is proposed using the spatiotemporal correlation among WSN data. Specifically, by introducing the historical data, joint low-rank constraint and temporal stability are utilized to further exploit the data spatiotemporal correlation. Furthermore, an algorithm based on the alternating direction method of multipliers is described to solve the resultant optimization problem efficiently. The simulation results show that the proposed method outperforms the state-of-the-art methods for different types of signal in the network.

Spikelike noise is a common type of random noise existing in many geoscience and remote sensing data sets. The attenuation of spike-like noise has become extremely important recently, because it is the main bottleneck when processing the simultaneous source data that are generated from the modern seismic acquisition. In this letter, we propose a novel low-rank decomposition algorithm that is effective in rejecting the spike-like noise in the seismic data set. The specialty of the low-rank decomposition algorithm is that it is applied along the morphological direction of the seismic data sets with a prior knowledge of the morphology of the seismic data, which we call local slope. The seismic data are of much lower rank along the morphological direction than along the space direction. The morphology of the seismic data (local slope) is obtained via a robust plane-wave destruction method. We use two simulated field data examples to illustrate the algorithm workflow and its effective performance.

The low-rank approximation method is one of the most effective approaches recently proposed for attenuating random noise in seismic data. However, the low-rank approximation approach assumes that the seismic data has low rank for its f - x domain Hankel matrix. This assumption is seldom satisfied for the complicated seismic data. Besides, the low-rank approximation approach is usually implemented in local windows in order to satisfy the principal assumption required by the algorithm itself. When implemented in local windows, the rank is even more difficult to choose because the seismic data is highly nonstationary in both time and spatial dimensions and the optimal rank for different local windows is not consistent with each other. In order to preserve enough useful energy, one needs to set a relatively large rank when implementing the low-rank approximation method, which makes the traditional method incapable of attenuating enough noise. Considering such difficulties described above, we propose an empirical low-rank approximation approach. We adaptively decompose the input data into several components that have truly low ranks via empirical mode decomposition. An interpretation of the proposed empirical low-rank approximation method is that we empirically decompose a multi-dip seismic image that is not of low rank into multiple single-dip seismic images that are low-rank individually. We use both synthetic and field data examples to demonstrate the superior performance of the proposed approach over traditional alternatives.

Bandpass filtering is a common way to estimate ground-roll noise on land seismic data, because of the relatively low-frequency content of ground roll. However, there is usually a frequency overlap between ground roll and the desired seismic reflections that prevents bandpass filtering alone from effectively removing ground roll without also harming the desired reflections. We apply a bandpass filter with a relatively high upper bound to provide an initial imperfect separation of ground roll and reflection signal. We then apply a technique called “local orthogonalization” to improve the separation. The procedure is easily implemented, since it involves only bandpass filtering and a regularized division of the initial signal and noise estimates. We demonstrate the effectiveness of the method on an open-source set of field data.

An important step in the construction of a support vector machine (SVM) is to select optimal hyperparameters. This paper proposes a novel method for tuning the hyperparameters by maximizing the distance between two classes (DBTC) in the feature space. With a normalized kernel function, we find that DBTC can be used as a class separability criterion since the between-class separation and the within-class data distribution are implicitly taken into account. Employing DBTC as an objective function, we develop a gradient-based algorithm to search the optimal kernel parameter. On the basis of the geometric analysis and simulation results, we find that the optimal algorithm and the initialization problem become very simple. Experimental results on the synthetic and real-world data show that the proposed method consistently outperforms other existing hyperparameter tuning methods.

To the problem of no overall optimal merger for one-way merger in the segmentation algorithm proposed by Wang et al., (1999), we propose a method of overall optimal search and merger. At the same time, for the problem of merging a segment which has non-value (value-segment) and a segment whose values are zeros entirely (zeros-segment) to a large segment in Wang's method, we also propose a corresponding method to solve the problem. The main techniques use the local cosine transform (LCT) algorithm for a single small segment, rather than folding processing using its original neighboring data, instead of making zero extension, and then fold the each zero-extension segment. A great deal of numerical simulations validate that this new improved technique solves several problems of the binary-based segment algorithm and Wang's segment algorithm; it not only obtains adapted effective segmentation results, but also there are not many redundancy segmentations.