In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observati...
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In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different are
The Bayesian optimization algorithm uses Bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its applicati...
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The Bayesian optimization algorithm uses Bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its application process, such as excessive computational complexity. To solve various problems in Bayesian algorithm, reduce its computational complexity, and enable it to better achieve image segmentation. The study chooses to improve the Bayesian algorithm on the basis of immune algorithm, and solves the problem of computational complexity by reducing the number of Bayesian network construction times, thereby improving the individual fitness of the population. Through simulation experiments, it has been shown that the average number of times the improved Bayesian algorithm reaches the optimal value is 30, which is higher than the traditional algorithm's 20 times. Its excellent optimization ability searches for the optimal threshold to complete image segmentation. The improved Bayesian optimization algorithm based on immune algorithm can effectively reduce computational complexity, shorten computational time, and improve convergence. And applying Bayesian algorithm to image segmentation has broadened the application field of the algorithm and found new exploration directions for image segmentation.
Traditional intelligent algorithm cannot optimize multiple parameters at the same time,so the optimization effect is *** order to solve such problem,swarm intelligence optimization algorithm is used in the antenna ***...
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Traditional intelligent algorithm cannot optimize multiple parameters at the same time,so the optimization effect is *** order to solve such problem,swarm intelligence optimization algorithm is used in the antenna *** analyzing the influence parameters,we design the objective function and adopt chaos optimization algorithm to optimize the initial population selection of particle swarm ***,according to the actual design requirements,the improved particle swarm optimization(PSO) algorithm is used to solve multiple optimization problems,and the optimal design of the antenna is also *** simulation results show that our scheme optimizes the design effect,and the antenna has larger directional gain,which effectively improves the comprehensive performance of antenna.
This study investigates the spatiotemporal variability of snow cover and seasonally frozen ground in northern China and Mongolia during 1988-2010 with passive microwave remote sensing records. We used the Goodison sno...
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This study investigates the spatiotemporal variability of snow cover and seasonally frozen ground in northern China and Mongolia during 1988-2010 with passive microwave remote sensing records. We used the Goodison snow algorithm, adapted by introducing an additional soil freeze/thaw indicator to improve its efficiency in mountainous areas, and soil freeze-thaw algorithm to estimate snow cover onset, duration, ablation and, for the first time, interval between snow cover ablation and thawing of seasonally frozen ground. Snow cover onset, duration, and ablation tended to vary systematically from high to low latitudes, and to trend toward early/long/late in elevated areas. The ablation-thawing interval varied from low to high latitudes/elevations, and from dry to relatively humid areas, being shorter (<2 weeks) in the north and elevated areas but longer in some cold-dry and plain-mountain adjacent regions. During 1988-2010, snow cover showed a later/earlier trend of the onset/ablation on the western Tibetan Plateau and a belt from northeast China to central Mongolia, with trends being stronger in spring than in autumn. The time of snow cover ablation was negatively correlated with maximum temperature in the northern study area, indicating that temperature mainly advanced snow melting in spring. However, no significant relationship between temperature and the interval was observed, suggesting that other unknown factors impact the interval. Furthermore, in the north and on Mt. Changbai the interval changed by <2 weeks, whereas changes were larger in cold-dry and plain-mountain transitional areas, indicating changes of Earth surface systems in those areas. (C) 2014 Elsevier B.V. All rights reserved.
Little is known about the shifts of frozen ground boundary in response to temperature variations in the East Asia. We therefore examined the relationship between changes of frozen ground boundary and temperature at no...
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Little is known about the shifts of frozen ground boundary in response to temperature variations in the East Asia. We therefore examined the relationship between changes of frozen ground boundary and temperature at northern China and Mongolia. Significant relationships were found between the boundaries' shifting and monthly average air temperature in northeast of China and in the northeast of Mongolia, where the higher temperature resulted in the more northward boundary of frozen ground. However, no significant relationship was found in northwest of Mongolia, in west of China, and in the west of Tibetan Plateau. These results indicate that the temperature is not the major factor in driving the boundary of seasonally frozen ground shifting at typical mid-latitude areas in Asia. This research demonstrates seasonally frozen ground dynamic response to climatic change at some mid-latitude areas where seasonally frozen ground boundary shifting would be utilized as an additional factor for tracking climatic change. Copyright (c) 2013 Royal Meteorological Society
Water quality parameter is the basic data of the river water quality mathematical model for forecasting river water quality status. In this paper, the parameter estimation problem of the analytical model, which is use...
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ISBN:
(纸本)9783037855508
Water quality parameter is the basic data of the river water quality mathematical model for forecasting river water quality status. In this paper, the parameter estimation problem of the analytical model, which is used to describe one-dimensional tracing test data of river streams with tracers instantaneously injected, is converted to the function optimization problem. And particle swarm optimization algorithm is applied to solve this problem. The experimental results show that the particle swarm optimization algorithm can estimate the water quality model parameter values regardless of whether the randomly sampling data has noise.
The advent of modern railway signalling and train control technology allows the implementation of advanced real-time railway management. Optimisation algorithms can be used to: minimise the cost of delays;find solutio...
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The advent of modern railway signalling and train control technology allows the implementation of advanced real-time railway management. Optimisation algorithms can be used to: minimise the cost of delays;find solutions to recover disturbed scenarios back to the operating timetable;improve railway traffic fluidity on high capacity lines;and improve headway regulation. A number of researchers have previously considered the problem of minimising the costs of train delays and have used various optimisation algorithms for differing scenarios. However, little work has been carried out to evaluate and compare the different approaches. This paper compares and contrasts a number of optimisation approaches that have been previously used and applies them to a series of common scenarios. The approaches considered are: brute force, first-come-first-served, Tabu search, simulated annealing, genetic algorithms, ant colony optimisation, dynamic programming and decision tree based elimination. It is found that simple disturbances (i. e. one train delayed) can be managed efficiently using straightforward approaches, such as first-come-first-served. For more complex scenarios, advanced methods are found to be more appropriate. For the scenarios considered in this paper, ant colony optimisation and genetic algorithms performed well, the delay cost is decreased by 30% and 28%, respectively, compared with first-comefirst-served. (C) 2012 Elsevier Ltd. All rights reserved.
Introduction Blood glucose ( BG) control performed by intensive care unit ( ICU) nurses is becoming standard practice for critically ill patients. New ( semi- automated) 'BG control' algorithms ( or 'insul...
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Introduction Blood glucose ( BG) control performed by intensive care unit ( ICU) nurses is becoming standard practice for critically ill patients. New ( semi- automated) 'BG control' algorithms ( or 'insulin titration' algorithms) are under development, but these require stringent validation before they can replace the currently used algorithms. Existing methods for objectively comparing different insulin titration algorithms show weaknesses. In the current study, a new approach for appropriately assessing the adequacy of different algorithms is proposed. Methods Two ICU patient populations ( with different baseline characteristics) were studied, both treated with a similar 'nursedriven' insulin titration algorithm targeting BG levels of 80 to 110 mg/ dl. A new method for objectively evaluating BG deviations from normoglycemia was founded on a smooth penalty function. Next, the performance of this new evaluation tool was compared with the current standard assessment methods, on an individual as well as a population basis. Finally, the impact of four selected parameters ( the average BG sampling frequency, the duration of algorithm application, the severity of disease, and the type of illness) on the performance of an insulin titration algorithm was determined by multiple regression analysis. Results The glycemic penalty index ( GPI) was proposed as a tool for assessing the overall glycemic control behavior in ICU patients. The GPI of a patient is the average of all penalties that are individually assigned to each measured BG value based on the optimized smooth penalty function. The computation of this index returns a number between 0 ( no penalty) and 100 ( the highest penalty). For some patients, the assessment of the BG control behavior using the traditional standard evaluation methods was different from the evaluation with GPI. Two parameters were found to have a significant impact on GPI: the BG sampling frequency and the duration of algorithm application. A hig
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