Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact

The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.

Geochemical and Geostatistical Studies for Estimating Gold Grade in Tarq Prospect Area by K-Means Clustering Method

Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.

Application of K-means and PCA approaches to estimation of gold grade in Khooni district(central Iran)

Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.

Three Indication Variables and Their Performance for the Troubled-Cell Indicator using K-Means Clustering

In Zhu,Wang and Gao(SIAM J.Sci.Comput.,43(2021),pp.A3009–A3031),we proposed a new framework of troubled-cell indicator(TCI)using K-means clustering and the numerical results demonstrate that it can detect the troubled cells accurately using the KXRCF indication variable.The main advantage of this TCI framework is its great potential of extensibility.In this follow-up work,we introduce three more indication variables,i.e.,the TVB,Fu-Shu and cell-boundary jump indication variables,and show their good performance by numerical tests to demonstrate that the TCI framework offers great flexibility in the choice of indication variables.We also compare the three indication variables with the KXRCF one,and the numerical results favor the KXRCF and the cell-boundary jump indication variables.

基于谱聚类的主动配电网多时间尺度无功优化策略

高比例分布式光伏接入配电网后,传统优化方案无法有效平抑电压波动,分布式光伏逆变器的无功调控能力难以充分利用。为此,提出一种基于谱聚类的主动配电网多时间尺度无功优化策略,该方法分为日前优化和日内实时优化两个阶段。首先,对离散设备的时间耦合性进行解耦,以配电网网损、平均电压偏差、电压波动严重程度为目标函数,建立基于社交网络搜索算法的日前无功优化模型,确定离散设备静态最优档位序列;其次,通过谱聚类的方法进行耦合,确定离散设备动态最优档位序列,结合改进的分布式光伏逆变器就地控制策略,建立日内实时优化模型,从而抑制日前预测数据偏差导致的电压波动;最后,基于改进后的IEEE33节点系统进行仿真实验。仿真结果表明,所提策略可以有效降低运算难度、提高求解效率,验证了该策略的有效性和优越性。

考虑初始风速与机端故障稳态电压跌落程度的直驱风机故障响应特性分类及其判别方法研究

对电力系统进行安全分析时,往往需要对各种预想场景进行仿真。随着风电的大量接入,对风电场提出适用于预想故障分析的等值建模方法变得十分重要,而根据风机故障后的动态特性对其进行分群则是等值建模的基础。各风机在故障后的动态响应特性不仅受其控制方式的影响,还受到运行风速和故障严重程度的影响,而一般情况下风机控制的响应速度较快,可以认为在故障持续期间风机能够运行至故障稳态。基于此,提出考虑初始风速及机端故障稳态电压跌落程度的风电机组故障响应特性分类及判别方法,以此作为风机分群的依据。首先针对永磁直驱风机(direct-driven permanent magnet synchronous generator,PMSG)故障过程中无功优先控制、故障恢复期间有功斜坡恢复的控制策略,分析其在不同风速条件下故障后所有可能的响应特性,将初始风速与故障稳态电压作为分类指标,从机理上找到直驱风机进入各类响应特性的边界条件;然后,提出利用预想故障信息的风机机端故障稳态电压计算方法,在此基础上提出适用于预想故障的直驱风机分群方法;最后,通过对风电场进行详细建模仿真,验证所提分群方法的正确性。

Incident Detection Based on Differential Analysis

Internet services and web-based applications play pivotal roles in various sensitive domains, encompassing e-commerce, e-learning, e-healthcare, and e-payment. However, safeguarding these services poses a significant challenge, as the need for robust security measures becomes increasingly imperative. This paper presented an innovative method based on differential analyses to detect abrupt changes in network traffic characteristics. The core concept revolves around identifying abrupt alterations in certain characteristics such as input/output volume, the number of TCP connections, or DNS queries—within the analyzed traffic. Initially, the traffic is segmented into distinct sequences of slices, followed by quantifying specific characteristics for each slice. Subsequently, the distance between successive values of these measured characteristics is computed and clustered to detect sudden changes. To accomplish its objectives, the approach combined several techniques, including propositional logic, distance metrics (e.g., Kullback-Leibler Divergence), and clustering algorithms (e.g., K-means). When applied to two distinct datasets, the proposed approach demonstrates exceptional performance, achieving detection rates of up to 100%.

蝉花优良菌株的选育

蝉花(Cordyceps chanhua)是一种药食两用虫草科真菌,是食品和保健品领域极具开发潜力的重要新原料。为探究野生蝉花不同分离方式对生物学特性和活性成分的影响,以大别山地区采集的5株野生蝉花为研究对象,采用分生孢子分离和组织分离(菌核分离)两种方法对野生蝉花进行分离,获取蝉花菌株10株,比较分析不同蝉花菌株的菌落形态、生长速度、子实体产量和活性物质含量等,使用归一化法进行品质综合评价和聚类热图分析。结果表明:分生孢子分离菌株与组织分离的菌株相比,除菌落生长速度外,子实体鲜重、子实体长度、子实体直径、生物学效率和活性成分等各项指标普遍占优,蝉花的菌落生长速度并不能作为菌株优劣的筛选依据,子实体产孢量与生物学效率和活性成分含量呈负相关;经比较分析发现4株综合品质较好的菌株,分别为IC1-1、IC3-1、IC1-2和IC2-1,子实体湿重在12.17~15.31 g之间,子实体长度在6.20~7.00 cm之间,子实体直径在2.15~2.75 mm之间,子实生物学效率在40.57%~51.03%之间,腺苷含量在1.76~1.89 mg·g^(-1)之间,多糖含量在46.1~50.1 mg·g^(-1)之间,N~6-(2-羟乙基)腺苷含量在1.21~1.33 mg·g^(-1)之间。该研究为蝉花资源保护和品种创制提供了理论依据。

范冠杰以活血化瘀药串治疗肥胖经验

范冠杰教授基于“动-定序贯八法”理论,认为肥胖除气虚、痰湿之外,还同时存在血瘀的病理因素;治疗肥胖时配伍活血化瘀药物为顺应疾病发展之需要。临证时可运用活血化瘀之山楂药串,该药串以山楂为君,配伍丹参-红花药对及牡丹皮-赤芍药对。山楂药串气血并治,以祛瘀为核心,辅以养血、行气血、健脾胃,散收兼施,动静相宜,适用于血脉瘀阻之肥胖患者。

An improved probabilistic load flow in distribution networks based on clustering and Point estimate methods

Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.

邱健主任益气活血和中解毒法辨治癥瘕积聚类疾病经验

摘录2则验案,介绍邱健主任运用益气活血和中解毒法辨治积聚类疾病的临床经验。邱主任临证辨治癥瘕积聚类疾病,主要传承国医大师孙光荣“中和”学术思想,注重整体观念,辨证施治与专病专方相结合,以益气活血、和解中焦少阳、扶正为关键,并结合体质差异、发病部位及病证特点,以自拟益气活血和中解毒汤为基础辨证加减,组方运用。

Speech detection method based on a multi-window analysis

Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.

The Activity and Theoretical Interpretation of Role of Trimethyl Phosphite Modified HZSM-5 Zeolite

The catalytic activity of trimethyl phosphite modified HZSM-5 zeolite and un-modified HZSM-5zeolite treated with 100% steam at 673,773,873,973 and 1073K, respectively, were investigated using heptanecracking as a probe reaction. The results showed that the heptane conversion of both trimethyl phosphitetreated samples and un-phosphated samples decreased with an increase in treating temperature, but trimethylphosphite modified samples showed higher activity in comparison with the un-modified samples, which weresteam-treated at a higher temperature. The results were firstly elucidated by the model cluster method andcomputational quantum chemistry method. Full optimization and frequency analysis of all cluster model havebeen carried out using the Gaussian 94 software-package with the PM 3 semi-empirical method performed onsmall cluster models. The computational results showed that the dealumination of trimethyl phosphite modi-fied zeolite model cluster was more difficult than that of un-modified zeolite model cluster when they weretreated with steam while investigating the heat of reaction.

Fe-Cr-W合金内富Cr团簇形核析出动力学的原子动力学蒙特卡罗模拟

低活化铁素体马氏体钢被认为是第四代堆及聚变反应堆候选结构材料之一。低活化铁素体马氏体钢以Fe-Cr为基体,W是其中重要的溶质元素。低温辐照脆化行为是限制低活化铁素体马氏体钢服役的重要问题之一。服役过程中析出的团簇因阻碍位错运动而引起材料硬化脆化。深入理解服役过程中团簇析出行为有助于认识低活化铁素体马氏体钢的低温辐照脆化问题。基于优化后的自主开发程序MIET_AKMC,利用原子动力学蒙特卡罗(atomic kinetic Monte Carlo, AKMC)方法研究了Fe-Cr(8%,10%,16%,20%)-W(1%,2%)合金在热老化条件下富Cr团簇形核析出动力学行为。结果表明,热老化后Cr原子析出形成富Cr的团簇,W仍保持固溶状态。Cr团簇析出分为形核、长大、粗化3个阶段。在团簇开始粗化时,团簇平均半径约(0.57±0.03) nm,且该值不受温度和Cr含量的影响。W会延迟团簇析出动力学过程,尤其是当Cr含量为10%时,延迟效果最明显,这是因为Cr含量为10%时,体系SRO参数绝对值达最大;W延迟效果得益于W与空位间较强的结合能(0.14 eV,空位与Cr结合能为0.06 eV)。从模拟结果可知,除已知的固溶强化外,W元素加入会通过延迟富Cr团簇析出从而有益于Fe-Cr钢性能,因为富Cr团簇会引起Fe-Cr合金硬化、脆化。

不同初加工方法对滇龙胆药材品质的影响

采集临沧云县产新鲜滇龙胆,采用9种不同初加工方法,以滇龙胆的根及根茎为研究对象,测定干燥后滇龙胆样品中龙胆苦苷、马钱苷酸、獐牙菜苦苷和当药苷含量,以及总灰分、酸不溶性灰分和浸出物等指标,依据《中华人民共和国药典》标准,对药材品质进行综合评价。结果表明,不同加工方法滇龙胆有效成分含量有一定差异,其中水洗烘干(SXHG)处理样品龙胆苦苷和马钱苷酸的含量显著高于其他处理,同时,所需干燥时间最短,仅为12 h,远低于其他处理;水洗烘干(SXHG)处理样品的总灰分、酸不溶性灰分和浸出物均达到《中华人民共和国药典》标准;主成分分析可知,水洗烘干(SXHG)滇龙胆样品综合质量最好,免洗阴干(MXYG)综合质量次之,但该方法加工的样品总灰分和浸出物未达到《中华人民共和国药典》标准,水洗阴干(SXYG)在传统生产中农户广泛使用,可根据天气、场地等实际情况在产地加工中作为辅助干燥方法。综合考虑,滇龙胆药材产地加工方法以水洗烘干为宜;若药材加工量较大,可根据天气、场地等实际情况采用水洗阴干方法进行加工。

考虑出力与电价不确定性的光伏集群有功功率分配方法

针对配网高渗透率光伏集群经济运行以及参与系统功率调整问题,提出一种区域光伏集群的有功功率优化调度分配方法。首先,将集群内部光伏站按照储能配置与受控能力进行分类;以多场景法将光伏出力、市场电价不确定性问题转化为确定性场景,引入条件风险价值量化不确定风险;建立区域光伏集群功率分配优化模型,以集群运行成本最小为优化目标,求解最优的日前出力计划与储能运行策略;最后,针对系统扰动导致运行方式改变,提出光伏集群参与系统调整的功率重新分配策略。算例结果表明区域光伏集群具有参与系统功率调整能力,验证了本文方法的有效性。

High-quality development of China’s power industry: Measurement, spatial pattern, and improvement paths

As the largest manufacturing country,China is striving to improve the development quality of its power industry with the goal of Carbon Peaking and Carbon Neutrality,in order to sustain its high-quality economic growth.In this regard,it is of importance to reveal both the regional development level of China’s power sector and its characteristics in terms of inspiring the next improvement direction.Motived by this purpose,this paper constructs an evaluation indicator system from three dimensions at the province level based on the connotation of high-quality development of the power industry(HDPI).Next,it calculates the HDPI indexes of 30 provinces and explore their development trend and spatial pattern.The results indicate that the total comprehensive performance of all regions was improved in general in the recent decade,but the spatial distribution characteristics of clean,low-carbon,safe and efficient are different.In the aspects of improvement space in future,not only do actively ameliorate the related management regimes or technical fields so as to improve the corresponding indicators’value,but also passively rely on the macro-development such as China’s urbanization level improvement,technological level improvement,and industrial structure upgrading as usual.

基于改进FCM的风电场有功功率分配

考虑到风电场功率分配存在功率跟踪精度差、波动剧烈和变桨机构动作频繁等问题,提出一种加入信息熵权值设置的FCM聚类算法来对风电场内机组进行分类,并以此为基础对风电场有功功率分配进行优化。采集风电场和调度中心的实时运行数据,选择风电机组的实时出力、实时风速、转速和桨距角作为聚类指标。运用熵权法对各指标进行权重设置,以此为基础对风电机组进行FCM聚类,再进行有功功率分配。以Matlab/Simulink中的SimWindFarm工具箱为平台,对12台风电机组进行有功功率分配仿真研究。通过与传统有功功率分配算法相比较,仿真验证所提分配算法可以有效提高功率跟踪精度、减小波动及机组动作次数。

间歇性分布式电源在主动配电网中的优化配置

以分布式风电和光伏为代表的间歇性分布式电源IDG(Intermittent Distributed Generator)得到了快速发展。考虑风速、光照强度和负荷之间的时序相关性,以年碳排放量最小为目标建立了IDG在主动配电网中的多场景优化配置模型。该模型能够计及调节有载调压变压器抽头、切除IDG出力和调节IDG功率因数3种主动管理措施。利用K-means聚类法对场景数量进行缩减并得到每个场景发生的概率。提出采用自适应遗传算法和原对偶内点法相结合的混合求解策略对模型进行求解。IEEE 33节点主动配电网算例仿真结果验证了所提模型与方法的有效性。

变化环境下水文资料序列的可靠性与一致性分析

由于气候变化和人类活动的双重影响,实测水文系列会发生一定程度变化,而水文资料的非一致性直接影响到水文模型参数的代表性,影响到水文模拟的精度和可靠性。选择黄河流域和淮河流域的两个典型支流,采用有序聚类法和Mann-Kendall检验法对其水文序列资料进行突变检测,结果表明,黄河流域黑石关站的水文序列在1969年以后发生了较大的变化,而淮河流域息县站的水文序列未发生明显变化,说明黑石关站水文系列自1969年以来受气候变化和人类活动等因素的影响比较大,在一定程度上破坏了该站水文序列的一致性。相比而言,息县站流域受气候变化和人类活动的影响比较小,水文序列的稳定性较好。