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RISS 인기검색어
- 검색키워드
- 저자 : Shi Shengzhao (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Selective and Fast-Response Fluorescent Probes for Hypochlorite and their Application
Lei Shi,Qinhai Chen,Haojia Hong,Guang Shao,Shengzhao Gong,Hua Xiang 대한화학회 2019 Bulletin of the Korean Chemical Society Vol.40 No.7
It is extremely important to develop selective and sensitive fluorescent probes for the detection of hypochlorite (HClO/ClO?). Herein, two ESIPT-based fluorescent probes (BS1 and BS2) were designed and prepared for the fast determination of HClO. The probes exhibited excellent specificity towards HClO and showed the rapid and huge fluorescent responses. Moreover, the probes BS1 and BS2 could sensitively detect HClO in the range of 0?100??M with the low detection limits of 87 and 106?nM, especially. Besides, the success of practical application in water samples and test strips suggested that the synthetic probes may be effective tools for the detection of hypochlorite in environmental samples.
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Improve coati optimization algorithm for solving constrained engineering optimization problems
Jia Heming,Shi Shengzhao,Wu Di,Rao Honghua,Zhang Jinrui,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (i) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half wait beneath to catch it and (ii) Coatis avoidance predators behavior, which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm’s performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coati optimization algorithm (ICOA) to enhance the algorithm’s efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm’s exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm’s global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems.
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A Ratiometric Fluorescent Probe for Selective Detection of Hypochlorite Anion
Zhijian Ou,Lei Shi,Wenli Huang,Shengzhao Gong,Haomei Liang,Haojia Hong 대한화학회 2017 Bulletin of the Korean Chemical Society Vol.38 No.12
A new fluorescent probe based on rhodamine and naphthalimide was synthesized for the discrimination of hypochlorite anion. Upon addition of NaClO, the emission intensities ratio (I576 nm/I528 nm) of the probe 1 increased quickly accompanied with the obvious change of color. The probe 1 also exhibited highly selectivity and fast response to hypochlorite anion. Furthermore, the newly proposed probe has been applied for natural water samples, and a satisfied result was obtained.
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Research on Data Intrusion Detection Technology based on Fuzzy Algorithm
Sheng Zhao,Huishan Han,Xuekui Shi 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.8
The computer system is becoming more complex and massive network data, which brings great difficulties to the traditional intrusion detection system. Intrusion detection system is an important part of the network and information security architecture, which is mainly used to distinguish the normal activities of the system and the suspicious and intrusion patterns. But the challenge is how to effectively detect network intrusion behavior in order to reduce the false alarm rate and false negative rate. Based on the shortcomings of existing intrusion detection methods, the fuzzy C- means clustering method is proposed to analyze the intrusion detection data, so as to find out the abnormal network behavior patterns. By testing the CUP99 data set, the results show that the IFCA is not only feasible but also accurate and efficient. The improved fuzzy clustering algorithm proposed in this paper can improve the detection rate of intrusion detection and reduce the false detection rate, and can be widely used in intrusion detection system.
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