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Research on Password Detection Technology of IoT Equipment Based on Wide Area Network
Jia Qu 한국통신학회 2022 ICT Express Vol.8 No.2
At present, while the Internet of Things (IoT) devices bring convenience to people, security issues have led to an increasing number of threats to IoT security. Since IoT devices have a Web application system for device managers to operate, the system can view device information, control and configure device status, and its security is of great significance. Among the various authentication methods provided by IoT devices, the password information authentication mechanism is still a critical method for Web login. If the IoT device has a weak Web password, once a hacker discovers the device, it is straightforward to be attacked and implanted with malicious code to control the device and attack other devices in the network. In response to this problem, this paper designs a set of automatic detection frameworks for weak passwords for web application systems of IoT devices. Based on this framework, an automated weak password detection system was developed to detect weak Web passwords on IoT devices on the wide-area networks of Beijing, Shandong Province, and Zhejiang Province. A total of 12,179 devices with weak Web passwords were found, accounting for all discovered IoT devices of 7.58%, verifying the effectiveness of the proposed framework.
A Parallel Method of Deep Packet Inspection based on Message-Passing Interface
Jia-xing Qu,Guo-yin Zhang,Xi-zhong Wang,Jia-hui Liu,Da-hua Song 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.12
With the increasing number of cores in multicore processors, it is challenging task how to take advantage of powerful parallel computing for the deep packet inspection. This paper introduces the deep packet inspection with a parallel method which exploits the message-passing interface (MPI). The parallel procedure includes the master thread and the slave thread. The master assigns the data packet to the slave. The slave executes the string matching with rules for inspecting. Both the master and the slave communicate by using MPI functions. The experimental results show that the parallel method is suitable for the trend of the increasing number of cores in multicore processors. Moreover, when the number of threads is equal to the number of cores in multicore processors, the performance arrives at the maximum throughput.
A Data Cleaning Model for Electric Power Big Data Based on Spark Framework
Zhao-Yang Qu,Yong-Wen Wang,Chong Wang,Nan Qu,Jia Yan 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.3
The data cleaning of electrical power big data can improve the correctness, the completeness, the consistency and the reliability of the data. Aiming at the difficulties of the extracting of the unified anomaly detection pattern and the low accuracy and continuity of the anomaly data correction in the process of the electrical power big data cleaning, the data cleaning model of the electrical power big data based on Spark is proposed. Firstly, the normal clusters and the corresponding boundary samples are obtained by the improved CURE clustering algorithm. Then, the anomaly data identification algorithm based on boundary samples is designed. Finally, the anomaly data modification is realized by using exponential weighting moving mean value. The high efficiency and accuracy is proved by the experiment of the data cleaning of the wind power generation monitoring data from the wind power station.
An approach of seismic design for sheet pile retaining wall based on capacity spectrum method
Qu, Honglue,Li, Ruifeng,Hu, Huanguo,Jia, Hongyu,Zhang, Jianjing Techno-Press 2016 Geomechanics & engineering Vol.11 No.2
As the forefront of structural design method, capacity spectrum method can be applied conveniently, and through this method, deformation demand of structure can be considered. However, there is no research for the seismic application in the structure of sheet pile retaining wall to report. Therefore, focusing on laterally loaded stabilizing sheet pile wall, which belongs to flexible cantilever retaining structure and meets the applying requirement of capacity spectrum method from seismic design of building structure, this paper studied an approach of seismic design of sheet pile wall based on capacity spectrum method. In the procedure, the interaction between soil and structure was simplified, and through Pushover analysis, seismic fortification standard was well associated with performance of retaining structure. In addition, by comparing the result of nonlinear time history analysis, it suggests that this approach is applicable.
Jia-Yu Lv,Ning-Ning Zhang,Ya-Wei Du,Ying Wu,Tian-Qiang Song,Ya-Min Zhang,Yan Qu,Yu-Xin Liu,Jie Gu,Ze-Yu Wang,Yi-Bo Qiu,Bing Yang,Da-Zhi Tian,Qing-Jun Guo,Li Zhang,Ji-San Sun,Yan Xie,Zheng-Lu Wang,Xin 연세대학교의과대학 2021 Yonsei medical journal Vol.62 No.1
Purpose: The aim of this study was to compare the efficacy of liver transplantation (LT) and liver resection (LR) for hepatocellularcarcinoma (HCC) patients with portal vein tumor thrombus (PVTT) and to investigate risk factors affecting prognosis. Materials and Methods: A total of 94 HCC patients with PVTT type I (segmental PVTT) and PVTT type II (lobar PVTT) were involvedand divided into LR (n=47) and LT groups (n=47). Recurrence-free survival (RFS) and overall survival (OS) were comparedbefore and after inverse probability of treatment weighting (IPTW). Prognostic factors for RFS and OS were explored. Results: Two treatment groups were well-balanced using IPTW. In the entire cohort, LT provided a better prognosis than LR. Among patients with PVTT type I, RFS was better with LT (p=0.039); OS was not different significantly between LT and LR(p=0.093). In subgroup analysis of PVTT type I patients with α-fetoprotein (AFP) levels >200 ng/mL, LT elicited significantly longermedian RFS (18.0 months vs. 2.1 months, p=0.022) and relatively longer median OS time (23.6 months vs. 9.8 months, p=0.065). Among patients with PVTT type II, no significant differences in RFS and OS were found between LT and LR (p=0.115 and 0.335,respectively). Multivariate analyses showed treatment allocation (LR), tumor size (>5 cm), AFP and aspartate aminotransferase(AST) levels to be risk factors of RFS and treatment allocation (LR), AFP and AST as risk factors for OS. Conclusion: LT appeared to afford a better prognosis for HCC with PVTT type I than LR, especially in patients with AFP levels>200 ng/mL.
Wei Qu,Yabin Zhou,Yundong Sun,Ming Fang,Han Yu,Wenjuan Li,Zhifang Liu,Jiping Zeng,Chunyan Chen,Chengjiang Gao,Jihui Jia 한국미생물학회 2011 The journal of microbiology Vol.49 No.2
Innate and adaptive immune responses are activated in humans when Helicobacter pylori invades the gastric mucosa. Nitric oxide (NO) and reactive nitrogen species are important immune effectors, which can exert their functions through oxidation and S-nitrosylation of proteins. S-nitrosoglutathione and sodium nitroprusside were used as NO donors and H. pylori cells were incubated with these compounds to analyze the inhibitory effect of NO. The suppressing effect of NO on H. pylori has been shown in vitro. Furthermore,the proteins modified by S-nitrosylation in H. pylori were identified through the biotin switch method in association with matrix-assisted laser desorption ionization/time-of-flight tandem mass spectrometry (MALDITOF-MS/MS). Five S-nitrosylated proteins identified were a chaperone and heat-shock protein (GroEL),alkyl hydroperoxide reductase (TsaA), urease alpha subunit (UreA), HP0721, and HP0129. Importantly,S-nitrosylation of TsaA and UreA were confirmed using purified recombinant proteins. Considering the importance of these enzymes in antioxidant defenses, adherence, and colonization, NO may exert its antibacterial actions by targeting enzymes through S-nitrosylation. Identification of protein S-nitrosylation may contribute to an understanding of the antibacterial actions of NO. Our findings provide an insight into potential targets for the development of novel therapeutic agents against H. pylori infection.
Xu, Jia,Liu, Chang,Zhou, Lei,Tian, Feng,Tai, Ming-Hui,Wei, Ji-Chao,Qu, Kai,Meng, Fan-Di,Zhang, Ling-Qiang,Wang, Zhi-Xin,Zhang, Jing-Yao,Chang, Hu-Lin,Liu, Si-Nan,Xu, Xin-Shen,Song, Yan-Zhou,Liu, Jun,Z Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.2
Serum alpha-fetoprotein (AFP) is a significant marker for clinical diagnosis and prognosis evaluation in hepatocellular carcinoma (HCC) patients. However, some proportion of liver cancer patients are AFP-negative (AFP ${\leq}$20ng/ml). In order to study the differences between clinicopathological factors and prognosis of alpha-fetoprotein negative and positive patients, a total of 114 cases (41 AFP-negative and 73 AFP-positive) were selected for our research. By systematically statistical analysis, the results demonstrated that compared with AFP-negative patients, AFP-positive examples were more likely to feature cirrhosis nodules, non-complete neoplasm capsules, and a poor Edmondson-steiner grade. Furthermore, AFP-negative patients demonstrated a favorable long-term prognosis. By univariate analysis and multivariate analysis with Cox's proportional hazards model, multiple tumors were found to be independent risk factors for worse survival of AFP negative patients; however, less tumor-free margins, multiple tumors and Edmondson-steiner grades III/IV, proved to be independent risk factors leading to a poor prognosis of AFP positive cases. Finally, we can infer that high levels of AFP signify a highly malignant tumor and unfavorable prognosis.
Yueming Qu,Qiong Jia,Euee S. Jang(장의선) 한국방송·미디어공학회 2022 한국방송공학회 학술발표대회 논문집 Vol.2022 No.11
The process of deep learning usually needs to deal with massive data which has greatly limited the development of deep learning technologies today. Convolutional Neural Network (CNN) structure is often used to solve image classification problems. However, a large number of images may be required in order to train an image in CNN, which is a heavy burden for existing computer systems to handle. If the image data can be compressed under the premise that the computer hardware system remains unchanged, it is possible to train more datasets in deep learning. However, image compression usually adopts the form of lossy compression, which will lose part of the image information. If the lost information is key information, it may affect learning performance. In this paper, we will analyze the effect of image compression on deep learning performance on CNN-based cat and dog classification. Through the experiment results, we conclude that the compression of images does not have a significant impact on the accuracy of deep learning.