This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discre...This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.展开更多
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study pres...Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.展开更多
Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of...Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of rainstorm days in summer in Lianyungang area is large was established by the classical C5. 0 decision tree algorithm. The data samples in 48 years( accounting for about 80% of total number of samples)was as the training set of a model,and the training accuracy rate of the model was 95. 83%. The data samples in the remaining 14 years( accounting for about 20% of total number of samples) were used as the test set of the model to test the model,and the test accuracy of the model was 85. 71%. The results showed that the prediction model of number of rainstorm days in summer constructed by C5. 0 algorithm had high accuracy and was easy to explain. Moreover,it is convenient for meteorological staff to use directly. At the same time,this study provides a new idea for short-term climate prediction of number of rainstorm days in summer.展开更多
为减少温室气体的排放,以风电为代表的清洁能源大规模接入电网。如何消纳高占比、波动剧烈的风电,成为现代电力系统所面临的重要问题。在此背景下,将多端柔性直流输电系统(VSC based multi-terminal HVDC,VSCMTDC)对功率的灵活调节能力...为减少温室气体的排放,以风电为代表的清洁能源大规模接入电网。如何消纳高占比、波动剧烈的风电,成为现代电力系统所面临的重要问题。在此背景下,将多端柔性直流输电系统(VSC based multi-terminal HVDC,VSCMTDC)对功率的灵活调节能力纳入安全约束机组组合(security-constrained unit commitment,SCUC)问题中进行调控。设计日前机组组合、短期实时调节和滚动重调节三段式配合的调度框架,并基于列与约束生成算法(column-andconstraint generation,C&CG)设计三层迭代求解方法。通过该方法解决了传统二阶段鲁棒性机组组合偏于保守的弊端,有效提高了风电消纳。为了充分利用VSC换流站能独立调节有功、无功的优势,在SCUC结果的基础上进行无功电压优化,并基于Benders分解算法进行求解,有效降低了系统网损。最后,将所提模型应用于改进IEEE 30节点系统算例,验证模型的有效性和可行性。展开更多
基金supported by the BK21 FOUR funded by the Ministry of Education of Korea and National Research Foundation of Korea,a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 1615013176)IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(RS-2024-00438411).
文摘This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.
基金This research is funded by the National Natural Science Foundation of China(Grant Nos.41807285 and 51679117)Key Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(SKLGP2019Z002)+3 种基金the National Science Foundation of Jiangxi Province,China(20192BAB216034)the China Postdoctoral Science Foundation(2019M652287 and 2020T130274)the Jiangxi Provincial Postdoctoral Science Foundation(2019KY08)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)。
文摘Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.
文摘采用UHPLC-QE-Orbitrap MS技术结合网络分析和化学计量学建立钴胺素C(cblC)缺乏症的临床表型系统表征和预测模型,利用尝试解开其复杂性。基于UHPLC-QE-Orbitrap MS技术在正、负模式下采集的血液非靶向代谢组学图谱,利用数据驱动网络算法Connect the Dots(CTD)快速搜索高连通的扰动代谢物,化学计量学算法学习其组别间复杂微小变化模式。通过对两种临床表型(癫痫和代谢综合征)的研究,结果表明CTD算法识别出的扰动代谢物子集展示出高度的临床表型特异性,且涉及的富集通路扰动均被报道与癫痫和代谢综合征的致病机制密切相关。进一步,CTD算法能够量度高连通扰动代谢物间的协变信息,构建主要疾病模块系统地表征癫痫和代谢综合征的复杂致病机制。识别出的扰动代谢物作为特征变量集,采用5-折交叉验证,偏最小二乘判别分析、支持向量机和随机森林的受试者工作特征曲线下面积预测均值分别为0.849、0.897和0.909(癫痫),0.889、0.931和0.921(代谢综合征),马修斯相关系数预测均值分别为0.667、0.668和0.723(癫痫),0.686、0.696和0.787(代谢综合征)。上述结果表明了提出的计算方法在揭示cblC缺乏症的临床表型复杂性和指导其个性化诊断策略方面的有效性。
基金Support by Meteorological Open Research Foundation for the Huaihe River Basin(HRM201602)Foundation for Young Scholars of Jiangsu Meteorological Bureau(Q201708,KQ201802)+2 种基金Science and Technology Innovation Team Foundation for Marine Meteorological Forecast Technology of Lianyungang Meteorological BureauKey Technology R&D Program Project of Lianyungang City(SH1634)Special Project for Forecasters of Jiangsu Meteorological Bureau(JSYBY201811,JSYBY201812,JSYBY201810)
文摘Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of rainstorm days in summer in Lianyungang area is large was established by the classical C5. 0 decision tree algorithm. The data samples in 48 years( accounting for about 80% of total number of samples)was as the training set of a model,and the training accuracy rate of the model was 95. 83%. The data samples in the remaining 14 years( accounting for about 20% of total number of samples) were used as the test set of the model to test the model,and the test accuracy of the model was 85. 71%. The results showed that the prediction model of number of rainstorm days in summer constructed by C5. 0 algorithm had high accuracy and was easy to explain. Moreover,it is convenient for meteorological staff to use directly. At the same time,this study provides a new idea for short-term climate prediction of number of rainstorm days in summer.
文摘为减少温室气体的排放,以风电为代表的清洁能源大规模接入电网。如何消纳高占比、波动剧烈的风电,成为现代电力系统所面临的重要问题。在此背景下,将多端柔性直流输电系统(VSC based multi-terminal HVDC,VSCMTDC)对功率的灵活调节能力纳入安全约束机组组合(security-constrained unit commitment,SCUC)问题中进行调控。设计日前机组组合、短期实时调节和滚动重调节三段式配合的调度框架,并基于列与约束生成算法(column-andconstraint generation,C&CG)设计三层迭代求解方法。通过该方法解决了传统二阶段鲁棒性机组组合偏于保守的弊端,有效提高了风电消纳。为了充分利用VSC换流站能独立调节有功、无功的优势,在SCUC结果的基础上进行无功电压优化,并基于Benders分解算法进行求解,有效降低了系统网损。最后,将所提模型应用于改进IEEE 30节点系统算例,验证模型的有效性和可行性。
