Secured Green Communication Scheme for Interference Alignment Based Networks

Zhibin Xie,Xinquan Geng,Yunfei Chen,Kening Song,Benjamin Panful,Yajun Wang,Yinjie Su,Zhenkai Zhang,Ying Hu 한국통신학회 2020 Journal of communications and networks Vol.22 No.1

In this paper, a new security and green communicationscheme is proposed to the interference-alignment (IA) based networks. To achieve a secured communication, full-duplex receiversare utilized to transmit artificial noise (AN). Both the signals andthe ANs are used to harvest energy to realize green communication. For these reasons, the feasible conditions of this scheme areanalyzed first. Secondly, the average transmission rate, the secrecyperformance and the harvested energy are investigated. Thirdly,an optimization scheme of simultaneous wireless information andpower transfer (SWIPT) is given to optimize the information transmissionand the energy harvesting efficiency. Meanwhile, an improvedIA iteration algorithm is designed to eliminate both the ANand the interference. Furthermore, relay cooperation is consideredand its system performance is analyzed. The simulations show thatthe target average transmission rate is not affected by AN, whilethe secrecy performance can be greatly improved. The energy harvestingefficiency is also better than the traditional schemes. Asexpected, the average transmission rate further is improved withthe relay cooperation.

Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties

Hong Pan,Guoqing Gui,Zhibin Lin,Changhui Yan 대한토목학회 2018 KSCE Journal of Civil Engineering Vol.22 No.3

Structural systems are often exposed to harsh environment, while these environmental factors in turn could degrade the system over time. Their health state and structural conditions are key for structural safety control and decision-making management. Although great efforts have been paid on this field, the high level of variability due to noise and other interferences, and the uncertainties associated with data collection, structural performance and in-service operational environments post great challenges in finding information to assist decision making. The machine learning techniques in recent years have been gaining increasing attentions due to their merits capturing information from statistical representation of events and thus enabling making decision. In this study, the deep Bayesian Belief Network Learning (DBBN) was used to extract structural information and probabilistically determine structural conditions. Different to conventional shallow learning that highly relies on the quality of the hand-crafted features, the deep learning is an end-to-end method to encode the information and interpret vast amount of data with minimizing or no features. A case study was conducted to address the methods for structure under viabilities and uncertainties due to operation, damage and noise interferences. Numerical results revealed that the deep learning exhibits considerably enhanced accuracy for structural diagnostics, as compared to the supervised shallow learning. With predetermined training set, the DBBN could accurately determine the structural health state in terms of damage level, which could dramatically help decision making for further structural retrofit or not. Note that the noise interference could contaminate the data representation and in turn increase the difficulty of the data mining, though the deep learning could reduce the impacts, as compared to conventional shallow learning techniques.

Characterization of phytochemical profile of rhizome of artificial cultured Polygonatum sibiricum with multiple rhizome buds

Cheng Weiqing,Pan Zhibin,Zheng Hanjing,Luo Gelian,Liu Zhibin,Xu Suli,Lin Junhan 한국응용생명화학회 2023 Applied Biological Chemistry (Appl Biol Chem) Vol.66 No.-

Rhizome of Polygonatum sibiricum is both a renowned traditional Chinese remedy and a commonly consumed delicacy. Due to the escalating demand and excessive overexploitation, there has been a growing interest in the artificial cultivation of this plant in recent years. To assess the therapeutic benefits of artificially cultivated P. sibiricum, it is crucial to identify and classify its phytochemical components, which are the primary bioactive compounds found in its rhizome. In this study, the phytochemical profile of an artificially cultivated P. sibiricum rhizomes with multiple rhizome buds (ACM) was characterized by using untargeted UHPLC-Q-Orbitrap-MS based approach. In addition, two-wild-types P. sibiricum rhizomes, namely the wild-type with multiple rhizome buds (WTM) and the wild-type with single rhizome bud (WTS), were used for comparison. A total of 183 phytochemicals, including 20 alkaloids, 48 flavonoids, 33 phenolic acids, and 82 terpenoids, were tentatively identified. Generally, the phytochemical profile of ACM was comparable to that of WTM and WTS. In specific, most of the identified alkaloids and phenolic acids, and approximately half of the identified terpenoids, were not significantly different. Notably, several phytochemicals with potent therapeutic properties, such as epiberberine, laetanine, sinapic acid, curcumenol, were present in ACM. Additionally, artificial cultivation increased the abundance of geniposide and naringenin, which have been linked to cardioprotective effects. These findings provide valuable insights for the future utilization of artificially cultivated P. sibiricum.

Genetic variations in DROSHA and DICER and survival of advanced non-small cell lung cancer: a two-stage study in Chinese population

Shuangshuang Wu,Yun Pan,Songyu Cao,Jiali Xu,Yan Liang,Yan Wang,Lei Chen,Yunyan Wei,Chongqi Sun,Weihong Zhao,Zhibin Hu,Hongxia Ma,Hongbing Shen,Jianqing Wu 한국유전학회 2015 Genes & Genomics Vol.37 No.7

MicroRNAs (miRNA) are a class of small, noncoding RNA molecules involved in carcinogenesis. Genetic variations in miRNA processing genes may affect the biogenesis of miRNAs, and consequently affect miRNAs regulation and development and progression of human cancer. Therefore, we hypothesized that polymorphisms in two main miRNA biosynthesis genes (DROSHA and DICER) may modulate the survival of advanced non-small cell lung cancer (NSCLC) patients in China. We selected 36 common tagging SNPs in DROSHA and DICER and evaluated the associations of these SNPs with survival of advanced NSCLC patients by a two-stage study in Chinese Han population (discovery cohort: 303 patients; replication cohort: 340 patients). Thirty-six SNPs were detected in the discovery cohort and 12 promising SNPs were validated in the replication cohort. The results showed that DROSHA rs3805525 was marginally associated with the survival of NSCLC patients in the replication cohort (dominant model: HR 0.69, 95 % CI 0.46–1.03, P = 0.071), which was in the same direction as that in the discovery cohort. When combing all patients into one group, three SNPs (rs3805525, rs17410035 and rs7719497) in DROSHA showed significantly associations with NSCLC survival (additive model: HR 0.82, 95 % CI 0.68–0.99 for rs3805525; HR 0.79, 95 % CI 0.62–1.00 for rs17410035; HR 0.76, 95 % CI 0.62–0.93 for rs7719497). Additionally, the combined analysis of those three SNPs showed a significant locus-dosage effect between number of favorable alleles and death risk of NSCLC (Trend P = 0.002). Genetic variations in DROSHA might be associated with the survival of advanced NSCLC patients in Chinese population.

Data-Driven Support Vector Machine with Optimization Techniques for Structural Health Monitoring and Damage Detection

Guoqing Gui,Hong Pan,Zhibin Lin,Yonghua Li,Zhijun Yuan 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.2

Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.

Utilizing Structural Equation Modeling and Segmentation Analysis in Real-time Crash Risk Assessment on Freeways

Chengcheng Xu,Dawei Li,Zhibin Li,Wei Wang,Pan Liu 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.7

The study aimed to utilize Structural Equation Modeling (SEM) and K-means clustering for predicting real-time crash risks onfreeways. The SEM was used to transform a number of correlated traffic variables into four independent latent traffic factors, and toestablish the interrelationships among the traffic variables and crash risks. The segmentation analysis based on K-means clusteringwas then conducted to investigate the main traffic factors affecting crash risks in various traffic regimes. It was found that: (a) Themeasurement equations in SEM can effectively account for the correlations among traffic variables by transforming numerouscorrelated traffic variables into several latent traffic variables; (b) The SEM can both capture the direct and indirect effects of trafficflow variables on crash risks. This promotes a better understanding how traffic conditions affect crash risks; (c) The SEM producesmore accurate estimates of crash risks than existing modeling technique. It can increase the crash prediction accuracy by an averageof 7.6% compared with the commonly used logistic regression; and (d) Segmentation analysis results suggested that the trafficfactors contributing to crash risks are various across different traffic regimes. The proactive crash prevention strategies for differenttraffic regimes were discussed based on the findings in the segmentation analysis

An exchange bias observed in Tb/Cr/FeCo trilayers with ultrathin Cr layer at low temperature

Sun Li,Li Xiaoyan,Zhang Yiwei,Song Hengbo,Zhang Wen,Kou Zhaoxia,Zhang Dong,Liu Xiaoying,Fei Hongyang,Pan Mengmei,Zhao Zhibin,Zhai Ya 한국물리학회 2022 Current Applied Physics Vol.44 No.-

Positive exchange bias field (He) is observed in Tb/Cr (tCr)/FeCo trilayers at 5 K without cooling field, and negative He for Tb/FeCo bilayer. The negative He of Tb/FeCo implies the FM coupling at the interface due to Co and Fe dominate in the magnetization of the ferrimagnetic interlayer alloy of FeCo and Tb. With the inserting of Cr layer, this situation is broken, and the positive He implies the antiferromagnetic interlayer coupling. A peak of He = 6.0 mT for trilayers with tCr = 1.5 nm is corresponding to the minimum value of coercivity as a function of tCr at 5 K, which is used to study the effect of the cooling field (Hfc) on He as a function of temperature. It is found that Hfc of 100 mT triggers He from positive to negative at T ≤ 15 K. The magnetoresistance results also confirm the coexistence of multiple MR mechanisms in these trilayers.