Complexity Comparison for Drinkers' and Normal People's EEG Using Wavelet Entropy

Jiufu Liu,Lei Gao,Zaihong Zhou,Haiyang Liu,Zhengqian Wang,Wenyuan Liu,Jianyong Zhou 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.8

This paper investigates the influence of alcohol on brain complexity. Considering electro-encephalogram (EEG) has the nonlinear dynamics characteristic of time-varying and non-stationary, we introduce the wavelet entropy (WE) analysis. We denoise EEG signal by using wavelet decomposition, then calculate the wavelet entropy of the denoised signal and analyze the nonlinear complexity. In 64 conductive poles experiments and in different stimulus experiments for FP2 electrode's EEG, the drinkers' EEG wavelet entropy is greater than normal people's. The wavelet entropy of every conductive pole of drinkers’ or normal persons’ is inconformity.

Nonlinear Robust Adaptive Control of Electro-hydrostatic Actuators With Continuous Friction Compensation

Jiahui Liu,Jianyong Yao,Wenxiang Deng 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.4

Electro-hydrostatic actuators (EHAs) have gradually been applied in the flight control systems of multielectric/all-electric aircraft due to the high power-to-volume ratio and the absence of throttling loss as well as overflow loss. However, the existence of high-order dynamics, system nonlinearities, and uncertainties significantly limits the tracking performance of EHAs. This article developed a robust adaptive controller with continuous friction compensation to improve the precise control performance of an EHA with a variable load, nonlinear friction, parametric uncertainties, and unmodeled disturbances. A nonlinear robust control law is used to attenuate various disturbances, and an adaptive law is adopted to cope with parametric uncertainties. Additionally, a continuous friction model is used to describe the friction behavior of an EHA to achieve effective friction compensation and further enhance the motion performance. Moreover, the upper bounds of the matched and mismatched uncertainties can be updated in real-time via adaptive laws, which can reduce design conservatism to some degree. The Lyapunov stability analysis reveals that asymptotic performance can be guaranteed despite the presence of unmodeled disturbances and parametric uncertainties. Furthermore, the applicability of the designed control algorithm with continuous friction compensation is demonstrated with experimental results.

Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval

Zeng, Hui,Liu, Yanrong,Li, Siqi,Che, JianYong,Wang, Xiuqing Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

Genome-wide identification of polyphenol oxidase (PPO) family members in eggplant (Solanum melongena L.) and their expression in response to low temperature

Xiao Kai,Liu Xiaohui,Zhang Aidong,Zha Dingshi,Zhu WeiMin,Tan Feng,Huang Qianru,Zhou Yaru,Zhang Min,Li Jianyong,Wu Xuexia 한국원예학회 2022 Horticulture, Environment, and Biotechnology Vol.63 No.5

Browning of fresh-cut eggplant (Solanum melongena L.) reduces its sensory and nutritional qualities and further influences consumption. Polyphenolic oxidases (PPOs) are key enzymes involved in browning, but the mechanisms that regulate the expression of PPO genes are still unclear. Here, 12 SmPPO genes were identified and phylogenetic analysis clustered these genes into four branches. Protein and cis-regulatory element analyses showed that the SmPPO gene family has a conserved gene structure and diverse functions. Gene expression analysis in different tissues showed that the expression of SmPPO2, SmPPO3, SmPPO6, SmPPO7, and SmPPO10 was higher in the flesh of the browning-sensitive inbred line ‘36’ than in the flesh of the browning-resistant line ‘Fu’. Furthermore, almost all SmPPO genes in ‘36’ were upregulated at 4 °C and 36 °C compared with those in ‘Fu’, and the expression increased earlier after harvest. In addition, SmPPO1, SmPPO6, SmPPO7, and SmPPO10 expression was significantly elevated in ‘36’ after 2 days at 36 °C. These results suggest that SmPPOs are key modulators of eggplant browning and provide candidate genes for further research on the mechanisms regulating fruit browning.

Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval

( Hui Zeng ),( Yanrong Liu ),( Siqi Li ),( Jianyong Che ),( Xiuqing Wang ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1

This paper presents a novel convolutional neural network based multi-feature fusion learning method for nonrigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

Thermal analysis for the large precision EDM machine tool considering the spark energy during long-time processing

Zhaoxi Zhao,Yukui Wang,Zhen-long Wang,Jianyong Liu 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.2

Thermal error is a major factor influencing the accuracy of large precision electrical discharge machining (EDM) machine tools, especially when processing continuously for a long time. In this paper, a novel thermal analysis model was set up to identify the static and dynamic thermal behaviour of the large EDM machine tool. The thermal effect of multiple spark discharges is considered. An equivalent heat flux method was proposed to model the intermittent heat flux for the first time. Both the steady and transient analyses were applied to investigate the thermal equilibrium time of critical points. It is found that when the study point is far away from the heat source, the longer thermal equilibrium time is needed. And the thermal equilibrium time of the machine tool was also estimated. Verification experiment has been performed, indicating the simulation accuracy of 87 % on the temperature rise of the electrode. Moreover, on the displacement of the spindle, the simulated result matched with the experimental result in Z direction error of 7 %. Finally, suggestions for reducing the thermal deformation were proposed to further improve the machining accuracy of large EDM machine tools.

Illuminating the hepatotoxic mechanism of norcantharidin in rats using metabolomics analysis

Cheng Weina,Chen Qihong,Wang Xiaoning,Liu Liu,Li Xiaofei,Duan Cancan,Zhang Jianyong 대한독성 유전단백체 학회 2023 Molecular & cellular toxicology Vol.19 No.3

Norcantharidin (NCTD) has multiple antitumor effects. However, NCTD can induce significant hepatotoxicity and the mechanism of hepatotoxicity is not clear for now.This study aimed to explore the hepatotoxicity of NCTD in rat by ultra-performance liquid chromatography (UPLC) quadrupole time-of-flight (Q-TOF)-MS (UPLC/Q-TOF-MS) metabolomics.Serum biochemical indices including alanine aminotransferase (ALT) and total bilirubin (T-BIL) were significantly increased. Histopathological and ultrastructure results revealed that hepatocytes were damaged. Furthermore, the metabolomics results showed that 11 metabolites in serum and 8 metabolites in liver were differential metabolites for NCTD hepatotoxicity. Four metabolic pathways including the sphingolipid metabolism, purine metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism were the key metabolic pathways related to NCTD hepatotoxicity.The metabolomics analysis in this study reveal new clues on the hepatotoxicity mechanism of NCTD in rats. These findings have potential applications in the toxicity study of NCTD.

Experimental Study on the Pipe-Soil Interface under the Influence of Pipe Jacking Stagnation Time

Tianliang Li,Wen Zhao,Run Liu,Jianyong Han,Cheng Cheng 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.3

In the process of long-distance and large-diameter pipe jacking, thixotropic mud is generally injected into the outer surface of the pipe wall to reduce the frictional resistance between the pipe and the soil. The process of pipe jacking may be stopped due to various reasons such as pipe rupture and equipment damage. When the pipe is restarted after being stopped for a period of time, the interface mechanical properties usually change substantially, resulting in a substantial increase in frictional resistance compared to before the stop. However, the mechanical properties and shear mechanism of the pipe-soil interface after jacking is restarted have not been sufficiently investigated. In this paper, a series of gravelly sand-concrete direct shear tests are carried out, in which lubricant is injected into the interface between gravelly sand and concrete, and the effect of construction stagnation time is considered. The mechanical properties of the interface when the concrete pipe is restarted after stagnation is studied by the direct shear tests. The results show that the friction coefficient of pipe-soil interface increases with the stagnation time, which is determined by the thixotropic mud state and the content of gravelly sand involved in shear. In a short period of stagnation, the friction coefficient is determined by the cohesion caused by thixotropic mud and the friction angle produced by the gravelly sand involved in the shearing action. With the increase of stagnation time, the friction angle gradually becomes the decisive factor for the increase of friction coefficient.

Dynamic thermal behavior and thermal error prediction of spindle due to periodic jump motions in a large precision die-sinking EDM machine

Zhaoxi Zhao,Jia Zhang,Yukui Wang,Zhen-long Wang,Jianyong Liu,Chenghao Quan 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.7

Thermal error which has been widely studied in cutting machine tools, was ignored in the EDM machines in most cases, since there is usually no high-speed rotation for spindles. However, for large die-sinking EDM machines, due to heavy load of drive system and long processing cycle of large aeronautical parts, thermal error induced by jump motion has seriously impaired the machining accuracy and gradually been recognized. In this paper, the dynamic thermal behavior of spindle induced by periodic jump motions in large precision die-sinking EDM machine was studied for the first time. Noted that the Z-axis base and column show obvious temperature rise and the thermal error in Y direction is the largest, which is about 6.5 and 5 times compared with that in X and Z directions. Based on this, an efficient thermal error prediction model was presented. Thermal sensitive points were picked out through fuzzy clustering and correlation theory, taken as inputs of radial basis function (RBF) neural network to guarantee the accuracy. As a result, the prediction accuracy in X, Y and Z directions are 95.2 %, 92.5 % and 94.4 %, respectively. Finally, the effect of jump period on spindle thermal behavior was investigated, and suggestions for optimizing jump motion parameters were proposed to further improve the machining accuracy of large EDM machines.

Altered microRNA Expression Profiles of Extracellular Vesicles in Nasal Mucus From Patients With Allergic Rhinitis

Geping Wu,Guanghai Yang,Ruxin Zhang,Guangyin Xu,Ling Zhang,Wu Wen,Jianbing Lu,Jianyong Liu,Yan Yu 대한천식알레르기학회 2015 Allergy, Asthma & Immunology Research Vol.7 No.5

Purpose: Allergic rhinitis (AR) is an inflammatory disorder of the upper airway. Exosomes or extracellular vesicles are nanosized vesicles of endosomal origin released from inflammatory and epithelial cells that have been implicated in allergic diseases. In this study, we characterized the microRNA (miRNA) content of exosomes in AR. Methods: Extracellular vesicles were isolated from nasal mucus from healthy control subjects (n=10) and patients with severe AR (n=10). Vesicle RNA was analyzed by using a TaqMan microRNA assays Human Panel-Early Access kit (Applied Biosystems, Foster City, CA, USA) containing probes for 366 human miRNAs, and selected findings were validated with quantitative RT-PCR. Target prediction and pathway analysis for the differentially expressed miRNAs were performed using DIANA-mirPath. Results: Twenty-one vesicle miRNAs were up -regulated and 14 miRNAs were under-regulated significantly (P<0.05) in nasal mucus from AR patients when compared to healthy controls. Bioinformatic analysis by DIANA-mirPath demonstrated that 32 KEGG biological processes were significantly enriched (P<0.05, FDR corrected) among differentially expressed vesicle miRNA signatures. Among them, the B-cell receptor signaling pathway (P=3.709E-09), the natural killer cell-mediated cytotoxicity (P=8.466E-05), the T-cell receptor signaling pathway (P=0.00075), the RIG-I-like receptor signaling pathway (P=0.00127), the Wnt signaling pathway (P=0.00130), endocytosis (P=0.00440), and salivary secretion (P=0.04660) were the most prominent pathways enriched in quantiles with differential vesicle miRNA patterns. Furthermore, miR-30-5p, miR-199b-3p, miR-874, miR-28-3p, miR-203, and miR-875-5p, involved in B-cell receptor and salivary secretion signaling pathways, were selected for validation using independent samples from 44 AR patients and 20 healthy controls. MiR-30-5p and miR-199b-3p were significantly increased in extracellular vesicles from nasal mucus when compared to healthy controls, while miR-874 and miR-28-3p were significantly down-regulated. In addition, miRNA-203 was significantly increased in AR patients, while miRNA-875-5p was found to be significantly decreased in AR patients. Conclusions: This study demonstrated that vesicle miRNA may be a regulator for the development of AR.