Indirect Adaptive Robust Control Design for Course Tracking of Ships Subject to Unknown Control Coefficient and Disturbances

Jinbo Wu,Chenghao Zeng,Yifei Hu 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.6

For course control of ships with unknown control coefficient and model parameters, an indirect adaptive robust controller, in which the parameter estimation law and the control law are designed separately, is proposed. This design method can achieve not only excellent course control performance but also accurate parameter estimates for secondary purposes such as assisting in ship maneuvering decision. Firstly, a Nussbaum function is combined with the adaptive dynamic surface control method to design a strong robust controller which can ensure the stabilityof the closed-loop ship course control system in spite of parameter uncertainties, unknown control coefficient and disturbances. Secondly, the nonlinear model for ship steering is converted into linear form by using the X-swapping technique. And a modified least-squares identification algorithm is then proposed to estimate the unknown model parameters. The global uniform ultimate boundedness of all signals of the resulting closed-loop system is guaranteed via Lyapunov stability theory. Lastly, simulation results are executed to demonstrate the effectiveness of theproposed design method.

Investigation of the super-resolution methods for vision based structural measurement

Zhi Cong Chen,Lijun Wu,Zhouwei Cai,Chenghao Lin,Shuying Cheng,Peijie Lin 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.3

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

An Improved Reptile Search Algorithm with Ghost Opposition-based Learning for Global Optimization Problems

Jia Heming,Lu Chenghao,Wu Di,Wen Changsheng,Rao Honghua,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4

In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.

Long Noncoding RNA Expression Profiling During the Neuronal Differentiation of Glial Precursor Cells from Rat Dorsal Root Ganglia

Yunfei Dai,Wei Ma,Tong Zhang,Jinwei Yang,Chenghao Zang,Kuangpin Liu,Xianbin Wang,Jiawei Wang,Zhen Wu,Xingkui Zhang,Chunyan Li,Junjun Li,Xiangpeng Wang,Jianhui Guo,Liyan Li 한국생물공학회 2020 Biotechnology and Bioprocess Engineering Vol.25 No.3

Long noncoding RNAs (lncRNAs) play important roles in the process of cell fate determination. However, their function and expression profiles have not yet been systematically investigated during the transdifferentiation of glial precursor cells derived from dorsal root ganglia (DRG) in the peripheral nervous system. Our results demonstrated significant differences in gene architecture and expression among the three transcript types (lncRNA, mRNA, and TUCP). Distinct differences in transcript length, exon number, and ORF length were identified between lncRNAs and mRNAs after comparative analysis of their structure and sequence conservation. We found that the upregulated lncRNAs outnumbered the downregulated lncRNAs in glial precursor cells cultured with proBDNF antiserum compared with the levels in glial precursor cells cultured without proBDNF antiserum. By a series of GO and KEGG analyses, we found that the effects of some lncRNAs on their target genes in cis were related to nerve growth factor-induced cell cycle, cell phenotype change, and neuronal differentiation. The qRT-PCR verification results of lncRNAs ENSRNOT00000091991, ENSRNOT00000087717, and LNC_000429 were mostly consistent with the sequencing results. The candidate lncRNAs may be associated with the neuronal transdifferentiation of glial precursor cells. Our study provides the first evidence for a remarkably diverse pattern of lncRNA expression during neuronal differentiation of glial precursor cells from rat DRG, and also provides a resource for lncRNA studies in the field of cell differentiation.

Ammonia etched petroleum pitch-based porous carbon as efficient catalysts for CO2 electroreduction

Ning Hui,Guo Zhihao,Wang Wenhang,Wang Xiaoshan,Yang Zhongxue,Ma Zhengguang,Tian Yangming,Wu Chenghao,Hao Jian,Wu Mingbo 한국탄소학회 2022 Carbon Letters Vol.32 No.3

Electrochemical reduction of carbon dioxide to valuable chemicals is a promising way of storing renewable energy through electric-to-chemical energy conversion, while its large-scale application is in urgent need of cheap and high-performance catalysts. Herein, we invent a convenient method to synthesize N-doped porous carbon by ammonia etching the pyrolysis carbon of petroleum pitch. We found the ammonia etching treatment not only increase the pyridinic-N content, but also enlarge the specific surface area of the petroleum pitch-based porous carbon. As a cheap and easily available catalyst for carbon dioxide electroreduction, up to 82% of Faradaic efficiency towards carbon monoxide was obtained at − 0.9 V vs the reversible hydrogen electrode in 0.1 M KHCO3. After a long time electrocatalysis of more than 20 h, the Faradaic efficiency of carbon monoxide remains 80%, indicating the porous carbon as made have an ultra-high stability as catalyst for carbon dioxide reduction. Our work provides a new technology to economically prepare efficient electrocatalysts for carbon dioxide reduction.

The characteristics of CO2 front dynamic migration in low permeability sandstone oil reservoirs under different miscibility degrees

Jin Yong,Wang Zhilin,Zhang Zhenxin,Lin Bo,Ge Zhengjun,You Qidong,Chen Hao,Liu Xiliang,Xu Chenghao,Gao Shuaiqiang,Wu Yi,Abro Zafar 한국자원공학회 2023 Geosystem engineering Vol.26 No.4

CO2 flooding is a significant technology for oil recovery, to reveal the microcosmic mechanism of CO2 flooding, CO2 slim tube experiments, long core CO2 flooding experiments, gas chromatography analysis experiments, and numerical simulations were carried out. The results show that the miscibility increase can improve the effect of dissolution and diffusion in the CO2-crude oil system, easing the CO2 finger entry phenomena, and delaying gas breakthrough time. Besides, the CO2 -crude oil interaction is enhanced by increasing the pressure difference between the injection well and the production well, which improves the oil recovery of CO2 flooding. Besides, CO2 carries the light and medium components (C4 ~ 25) forward to cross the first miscible zone, which makes the CO2-crude oil system form double miscible zones. This study reveals the rule of CO2 front and light components front under different miscibility degrees of CO2 flooding from the microscopic perspective and deepens the theory of CO2 flooding in low-permeability sandstone reservoirs.