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- 저자 : Liu Simiao (검색결과 4 건)
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Meng Liu,Yilun Deng,Yajun Cheng,Zilong Hao,Simiao Wu,Ming Liu 연세대학교의과대학 2021 Yonsei medical journal Vol.62 No.7
Purpose: Plasma osmolality, a marker of dehydration, is associated with cardiovascular mortality. We aimed to investigatewhether elevated plasma osmolality is associated with case fatality within 1 year after severe acute ischemic stroke. Materials and Methods: We included severe ischemic stroke patients (defined as National Institutes of Health Stroke Scale ≥15score) within 24 hours from symptom onset admitted to the Department of Neurology, West China Hospital between January 2017and June 2019. Admission plasma osmolality was calculated using the equation 1.86*(sodium+potassium)+1.15*glucose+urea+14. Elevated plasma osmolality was defined as plasma osmolality >296 mOsm/kg, indicating a state of dehydration. Study outcomesincluded 3-month and 1-year case fatalities. Multivariable logistic regression was performed to determine independent associationsbetween plasma osmolality and case fatalities at different time points. Results: A total of 265 patients with severe acute ischemic stroke were included. The mean age was 71.2±13.1 years, with 51.3%being males. Among the included patients, case fatalities were recorded for 31.7% (84/265) at 3 months and 39.6% (105/265) at1 year. Elevated plasma osmolality (dehydration) was associated with 3-month case fatality [odds ratio (OR) 1.98, 95% confidenceinterval (CI) 1.07–3.66, p=0.029], but not 1-year case fatality (OR 1.51, 95% CI 0.84–2.72, p=0.165), after full adjustment for confoundingfactors. Conclusion: Elevated plasma osmolality was independently associated with 3-month case fatality, but not 1-year case fatality, forsevere acute ischemic stroke.
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A bioinspired flexible organic artificial afferent nerve
Kim, Yeongin,Chortos, Alex,Xu, Wentao,Liu, Yuxin,Oh, Jin Young,Son, Donghee,Kang, Jiheong,Foudeh, Amir M.,Zhu, Chenxin,Lee, Yeongjun,Niu, Simiao,Liu, Jia,Pfattner, Raphael,Bao, Zhenan,Lee, Tae-Woo American Association for the Advancement of Scienc 2018 Science Vol.360 No.6392
<P>The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.</P>
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Load Prediction Based on Optimization Ant Colony Algorithm
Li Wei,Tang Jingmin,Ma Han,Fan Min,Liu Simiao,Wang Jie 대한전기학회 2023 Vol.18 No.1
Short-term load in the power system is associated with huge computational consumption and low model utilization. Large input fl uctuation tends to increase the training error of the neural network prediction model and reduce its generalization ability. To cope with this problem, this study aimed to introduce a method of radial basis function neural network algorithm based on least squares support vector machine algorithm. Based on the electricity market in an area of Yunnan province, the short-term loads were forecasted with historical data. First, the ant colony algorithm was improved using the chaos theory. Second, the improved ant colony was used to search least squares support vector machine and output the optimal parameters of the model. Then, the optimized model was used to train the data samples, and the output regression machine was used to provide better structures and parameters for the radial basis function neural network. The fi ndings showed that compared with multiple prediction methods, the algorithm in this paper reduces the learning time and improves the fi tting degree of the algorithm on the basis of improving the prediction accuracy. It shows that the algorithm in this paper has great advantages and good application prospects.
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Load Prediction Based on Optimization Ant Colony Algorithm
Li Wei,Tang Jingmin,Ma Han,Fan Min,Liu Simiao,Wang Jie 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.1
Short-term load in the power system is associated with huge computational consumption and low model utilization. Large input fluctuation tends to increase the training error of the neural network prediction model and reduce its generalization ability. To cope with this problem, this study aimed to introduce a method of radial basis function neural network algorithm based on least squares support vector machine algorithm. Based on the electricity market in an area of Yunnan province, the short-term loads were forecasted with historical data. First, the ant colony algorithm was improved using the chaos theory. Second, the improved ant colony was used to search least squares support vector machine and output the optimal parameters of the model. Then, the optimized model was used to train the data samples, and the output regression machine was used to provide better structures and parameters for the radial basis function neural network. The findings showed that compared with multiple prediction methods, the algorithm in this paper reduces the learning time and improves the fitting degree of the algorithm on the basis of improving the prediction accuracy. It shows that the algorithm in this paper has great advantages and good application prospects.
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