To Tolerate or To Impute Missing Values in V2X Communications Data?

Misbehavior detection is a critical task in vehicular ad hoc networks. However, state-of-the-art data-driven techniques for misbehavior detection are usually conducted through complete V2X communication data collected from simulated experiments. This thesis evaluates the main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communication data. This thesis proposes three novel methods for imputing and tolerating missing data. The first two are novel imputation methods that are based on cooperative clustering and collaborative clustering. The latter is a missing-tolerant method that is an ensemble learning based on the random subspace selection and Dempster-Shafer fusion. The effectiveness of the proposed techniques is evaluated in the ground truth vehicular reference misbehavior data. Moreover, a multi-factor amputation framework has been developed to induce missingness over V2X communication data with different missing ratios, mechanisms, and distributions. This framework provides a comprehensive benchmark resembling missingness over V2X communication data. The proposed methods are compared with some missing-tolerant and imputation methods. The attained results over benchmark data are analyzed and indicated the winner treatments in each aspect

Cooperative Clustering Missing Data Imputation

Missing data imputation is a critical part of data cleaning tasks and vital for learning from incomplete data. This paper proposes a novel cooperative clustering imputation (CCI) method to estimate missing values. The proposed method aims to find a better clustering model and donor for imputation, comparing with individual clustering algorithms. It makes use of agreements among different clustering algorithms to generate a set of sub-clusters, and, then, merges these sub-clusters based on the matrix of the performance measures of sub-clusters. The proposed method is evaluated using ten public datasets from UCI data repository and V2X communication data with induced missing samples, and compared with three standard clustering based imputation methods, k-means imputation, fuzzy c-means imputation, and partition around medoids imputation. Missing values are induced through each dataset by different missing mechanisms, missing rates, and missing distribution, and, thus, various incomplete datasets are generated. The performance of these methods are checked using normalized root mean square error (NRMSE). The attained experimental results indicate the effectiveness of the proposed missing values imputation method

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods

COLI: Collaborative clustering missing data imputation

Missing data imputation plays an important role in the data cleansing process. Clustering algorithms have been widely used for missing data imputation, yet, there is little research done on the use of clustering ensemble for missing data imputation, which aggregates multiple clustering results. This paper proposes a novel collaborative clustering-based imputation method, called COLI, which uses the imputation quality as a key criterion for the exchange of information between different clustering results. To the best of our knowledge, this is the first study on the impact of collaborative clustering on imputation performance. The main contributions of this paper are three-fold. A novel missing value imputation based on collaborative clustering is proposed, three amputation strategies are used to induce missingness on various complete and publicly available datasets with different mechanisms, distributions, and ratios, which allows evaluating the imputation quality of the proposed method in estimating missing values of various numerical datasets with different missingness mechanisms, distributions, and ratios. The proposed method is compared to several state-of-the-art imputation methods and attained results demonstrate that the proposed method is an effective method for handling missing data

To Tolerate or To Impute Missing Values in V2X Communications Data?

Misbehavior detection is a critical task in vehicular ad hoc networks (VANETs). However, state-of-the-art data-driven techniques for misbehavior detection are usually conducted through complete V2X communications data collected from simulated experiments. This article evaluates main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communications data. This article proposes two novel methods for imputing and tolerating missing data. The former is a novel imputation method that is based on the collaborative clustering and the latter is a missing-tolerant method that is an ensemble learning based on the random subspace selection and Dempster-Shafer fusion. The effectiveness of the proposed techniques is evaluated by the ground-truth vehicular reference misbehavior (VeReMi) data. Moreover, a multifactor amputation framework has been developed to induce missingness over V2X communications data with different missing ratios, mechanisms, and distributions. This provides a comprehensive benchmark resembling missingness over V2X communications data. The proposed methods are compared with five missing-tolerant and nine imputation methods. The attained results over the benchmark data indicate that the proposed missing-tolerant method is significantly better than other treatment methods in terms of accuracy and F-measure

First-principles study of the properties for crystal Ge2Sb2Te5 with Ge vacancy

Ge2Sb2Te5 (GST) is a technologically important phase-change material for data storage, where the fast reversible phase transition between crystalline and amorphous states is used for recording information. The effects of vacancies on crystal GST were investigated by ab initio calculations. Based on analysis of the vacancy formation energy, the GST structure with Ge vacancy (VGe) was found to be the most stable. Thereafter, the influence of VGe defects on crystal GST structure was deliberated by analyzing the band structure, electron density difference, total density of states (TDOS) and partial density of states (PDOS) of GST structure. The results reveal that VGe can promote the Fermi level enter into the valence band, which makes the GST material exhibit more pronounced properties of P-type semiconductors. Nevertheless, VGe shows a slight effect on the chemical bond characters. When VGe concentration maintained at 20% in the GST structure, the band gap is the widest about 0.45eV. Moreover, VGe can result in the electrons in s orbital of Ge, Sb and p, d orbitals of Te make a contribution to the valence band, while electrons in p and d orbitals of Ge, Sb are more favorable to conduction band

Analysis on adaptability of determination methods for pigging cycle of wet-gas pipeline

In actual operation, the pigging period of wet-gas pipeline should be determined by some methods to guarantee the efficiency of pipeline transportation. In order to study the adaptability of common determination methods for pigging cycle of wet-gas pipeline in China, the disadvantages of minimum gas transmission efficiency method, maximum allowable pressure drop method and maximum liquid inventory method were analyzed. Additionally, the variation rules of pipeline transportation efficiency and liquid inventory after pigging were emphatically studied based on two actual pipelines. The results show that the key factor to determine the pigging cycle is the volume of liquid slug as a result of pigging rather than the liquid inventory in pipeline when the maximum liquid inventory method is adopted, and it is limited to determine the pigging cycle by static liquid inventory. In the case of large throughput of pipeline, the minimum gas transmission efficiency method and the maximum allowable liquid inventory method are also limited for the pigging cycle obtained is infinite. For actual pipelines, the pigging cycle obtained by the minimum gas transmission efficiency method or the maximum liquid inventory method is too short, which cannot provide guidance for determination of field pigging cycle

SET Domain Group 703 Regulates Planthopper Resistance by Suppressing the Expression of Defense-Related Genes

Plant defense responses against insect pests are intricately regulated by highly complex regulatory networks. Post-translational modifications (PTMs) of histones modulate the expression of genes involved in various biological processes. However, the role of PTMs in conferring insect resistance remains unclear. Through the screening of a T-DNA insertion activation-tagged mutant collection in rice, we identified the mutant planthopper susceptible 1 (phs1), which exhibits heightened expression of SET domain group 703 (SDG703). This overexpression is associated with increased susceptibility to the small brown planthopper (SBPH), an economically significant insect pest affecting rice crops. SDG703 is constitutively expressed in multiple tissues and shows substantial upregulation in response to SBPH feeding. SDG703 demonstrates the activity of histone H3K9 methyltransferase. Transcriptomic analysis revealed the downregulation of genes involved in effector-triggered immunity (ETI) and pattern-triggered immunity (PTI) in plants overexpressing SDG703. Among the downregulated genes, the overexpression of SDG703 in plants resulted in a higher level of histone H3K9 methylation compared to control plants. Collectively, these findings indicate that SDG703 suppresses the expression of defense-related genes through the promotion of histone methylation, consequently leading to reduced resistance against SBPH. The defense-related genes regulated by histone methylation present valuable targets for developing effective pest management strategies in future studies. Furthermore, our study provides novel insight into the epigenetic regulation involved in plant-insect resistance