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RISS 인기검색어
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- 저자 : Zhang Jinrui (검색결과 4 건)
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Research and Implementation of RGB-LED Mixed Light Matching along with Music Playing
Zhang Hong,Gao Xiaoshuang,Mohanmmed Obadi,Zhao Ruirui,Tong Jinrui,Tian Chunwei,Hu Zhiqiang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.4
Synesthesia effect between music and color is a researching direction which is concerned much. The problem of mismatching between music playing and color had always existed in applications. On the basis of researching music and color, colors which are corresponding to various chords respectively are determined, and the frequency spectrum analysis on chords is performed. The real-time frequency domain transformation to time domain audio signal being played is carried out. The closest chord to audio signal being played can be identified via comparison between a chord spectrum and the spectrum of audio signal being played, and the color corresponding to the audio signal is determined. To improve the accuracy of recognition the method is investigated with which wavelet transform is adopted to de-noise processing of audio signal. Through tests on the audio experiment platform, color mixed effect test, optimization of the audio signal processing parameters, and processing time sequence scheming have been performed. A novel audio player is developed in application to realize the optimal RGB-LED mixed light which automatically presents matching color effect for arbitrary music being played. The research can reveal relationships between music and color from the perspective of frequency spectrum, promote the development of the color music, which has broad application prospects in urban landscape, stage lighting, and psychotherapy.
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Hongyan Nie,Hongxin Liao,Jinrui Wen,Cuiqiong Ling,Liyan Zhang,Furong Xu,Xian Dong The Korean Society of Ginseng 2024 Journal of Ginseng Research Vol.48 No.2
Background: Fusarium oxysporum (F. oxysporum) is the primary pathogenic fungus that causes Panax notoginseng (P. notoginseng) root rot disease. To control the disease, safe and efficient antifungal pesticides must currently be developed. Methods: In this study, we prepared and characterized a nanoemulsion of Foeniculum vulgare essential oil (Ne-FvEO) using ultrasonic technology and evaluated its stability. Traditional Foeniculum vulgare essential oil (T-FvEO) was prepared simultaneously with 1/1000 Tween-80 and 20/1000 dimethyl sulfoxide (DMSO). The effects and inhibitory mechanism of Ne-FvEO and T-FvEO in F. oxysporum were investigated through combined transcriptome and metabolome analyses. Results: Results showed that the minimum inhibitory concentration (MIC) of Ne-FvEO decreased from 3.65 mg/mL to 0.35 mg/mL, and its bioavailability increased by 10-fold. The results of gas chromatography/mass spectrometry (GC/MS) showed that T-FvEO did not contain a high content of estragole compared to Foeniculum vulgare essential oil (FvEO) and Ne-FvEO. Combined metabolome and transcriptome analysis showed that both emulsions inhibited the growth and development of F. oxysporum through the synthesis of the cell wall and cell membrane, energy metabolism, and genetic information of F. oxysporum mycelium. Ne-FvEO also inhibited the expression of 2-oxoglutarate dehydrogenase and isocitrate dehydrogenase and reduced the content of 2-oxoglutarate, which inhibited the germination of spores. Conclusion: Our findings suggest that Ne-FvEO effectively inhibited the growth of F. oxysporum in P. notoginseng in vivo. The findings contribute to our comprehension of the antifungal mechanism of essential oils (EOs) and lay the groundwork for the creation of plant-derived antifungal medicines.
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Huaiqian Bao,Lijin Song,Zongzhen Zhang,Baokun Han,Jinrui Wang,Junqing Ma,Xingwang Jiang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.9
This study proposes a framework for bearing remaining useful life (RUL) prediction that uses multidomain features and a dual-attention mechanism (DAM). First, sparsity measures are introduced as new feature parameters to comprehensively and accurately extract the degradation features of bearings. Second, a long short-term memory network integrated with DAM is applied for RUL prediction. DAM simultaneously applies the attention mechanism to the time steps and feature dimension to increase the attention to important information and enhance the prediction performance of the network. Third, a pseudo-normalization method is proposed to solve the problem of unknown bearing test data in actual working conditions under the premise of retaining the original data characteristics and RUL prediction accuracy as much as possible. Lastly, the proposed framework is experimentally proven on public datasets and compared with other methods to prove its feasibility and effectiveness.
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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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