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algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs

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algorithmS 2025年第3期18卷 174-174页

作者： Liu, Binglin Zeng, Weijia Liu, Weijiang Peng, Yi Yao, Nini Nanning Normal Univ Sch Geog & Planning Nanning 530001 Peoples R China Nanning Normal Univ Key Lab Environm Change & Resources Use Beibu Gulf Minist Educ Nanning 530001 Peoples R China City Univ Hong Kong Coll Engn Hong Kong 999077 Peoples R China Southwest Univ Sch Geog Sci Chongqing 400715 Peoples R China Univ Nottingham Dept Architecture & Built Environm Ningbo 315154 Peoples R China

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

关键词： urban residential vacancy rate algorithm application data source diversity complex environment urban development needs

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application of Swarm Intelligence Optimization algorithm in Antenna Design

Application of Swarm Intelligence Optimization Algorithm in ...

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第六届智能交通、大数据及智慧城市国际会议

作者： Xiaodong Zheng School of electronic and electrical engineering Dongguan Polytechnic

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.

关键词： population intelligence optimization algorithm antenna design algorithm application particle swarm algorithm

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Spatial variations in snow cover and seasonally frozen ground over northern China and Mongolia, 1988-2010

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GLOBAL AND PLANETARY CHANGE 2014年 116卷 139-148页

作者： Han, Lijian Tsunekawa, Atsushi Tsubo, Mitsuru He, Chunyang Shen, Miaogen Chinese Acad Sci Ecoenvironm Sci Res Ctr State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Tottori Univ Arid Land Res Ctr Tottori 6800001 Japan Beijing Normal Univ State Key Lab Earth Surface Processes & Resource Beijing 100875 Peoples R China Beijing Normal Univ Coll Resources Sci & Technol Beijing 100875 Peoples R China Chinese Acad Sci Inst Tibetan Plateau Res Beijing 100101 Peoples R China

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.

关键词： passive microwave remote sensing climatic warming algorithm application remote sensing of frozen ground and snow

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Shifting of frozen ground boundary in response to temperature variations at northern China and Mongolia, 2000-2007

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INTERNATIONAL JOURNAL OF CLIMATOLOGY 2013年第7期33卷 1844-1848页

作者： Han, Lijian Tsunekawa, Atsushi Tsubo, Mitsuru Chinese Acad Sci Ecoenvironm Sci Res Ctr State Key Lab Urban & Reg Ecol Beijing Peoples R China Tottori Univ Arid Land Res Ctr Tottori 680 Japan

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

关键词： radar remote sensing climatic change algorithm application seasonally frozen ground boundary

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Optimization of Bayesian algorithms for multi-threshold image segmentation

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第4-5期24卷 2863-2877页

作者： Tian, Qiaoyu Xu, Wen Xu, Jin Sichuan Univ Sch Elect & Elect Informat Engn Jinjiang Coll Meishan Peoples R China

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.

关键词： Bayesian algorithm image segmentation immune algorithm merit-seeking capability Bayesian network multi threshold images algorithm application

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Glycemic penalty index for adequately assessing and comparing different blood glucose control algorithms

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CRITICAL CARE 2008年第1期12卷 R24-R24页

作者： Van Herpe, Tom De Brabanter, Jos Beullens, Martine De Moor, Bart Van den Berghe, Greet Katholieke Univ Leuven ESAT Res Div SCD B-3001 Heverlee Belgium LUDIT B-3001 Heverlee Belgium Katholieke Univ Leuven Univ Hosp Gasthuisberg Dept Intens Care Med B-3000 Louvain Belgium

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

关键词： Blood Glucose Blood Glucose Measurement Average Blood Glucose Tight Glycemic Control algorithm application

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A comparison of algorithms for minimising delay costs in disturbed railway traffic scenarios

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2012年第1-2期2卷 23-33页

作者： Fan, Bo Roberts, Clive Weston, Paul Univ Birmingham Dept Elect Elect & Comp Engn Birmingham B15 2TT W Midlands England

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.

关键词： Railway timetable rescheduling Disturbed railway traffic Optimisation Junction Minimising delay costs algorithm application

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Water quality model parameters estimation based on particle swarm optimization algorithm

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2nd International Conference on Energy, Environment and Sustainable Development (EESD 2012)

作者： Xu, Wenyuan Liu, Binxiang Xu, Xing Hu, Na Hu, Hao Zhu, Ying Jingdezhen Ceram Inst Coll Informat & Engn Jingdezhen City Peoples R China Jingdezhen Ceram Inst Humanities & Social Sci Coll Jingdezhen City Peoples R China Huaihua Univ Labrary Huaihua Univ Huaihua Peoples R China

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.

关键词： water quality model particle swarm optimization parameter estimation algorithm application water quality prediction

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