Residential Electricity Consumption Behavior Mining Based on System Cluster and Grey Relational Degree

In order to improve the utilization of the residential electricity consumption data which contains the information on the user’s electricity consumption habits, a residential electricity consumption behaviors mining algorithm model is constructed. Firstly, according to the attribute, the collected data can be divided into the global data and the phase data, then the appropriate global variables are selected to mine the user’s electricity consumption patterns in the near future on the system clustering algorithm. Based on the theory of grey relational analysis, combing phase data with the power modes to analyze the potential characteristics of residential electricity consumption behaviors deeply that verify the ability of latest power mode to predict household electricity consumption situation in the coming few days and the effect of dominant phase variables on the peak load shifting. Finally, from the actual data of a certain family, the proposed data mining algorithm is testified that it can effectively explore the electricity consumption behavior habits and characteristics of the family.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Cluster consensus of second-order multi-agent systems via pinning control

This paper investigates the cluster consensus problem for second-order multi-agent systems by applying the pinning control method to a small collection of the agents. Consensus is attained independently for different agent clusters according to the community structure generated by the group partition of the underlying graph and sufficient conditions for both cluster and general consensus are obtained by using results from algebraic graph theory and the LaSalle Invariance Principle. Finally, some simple simulations are presented to illustrate the technique.

A New Model for Strategy Formulation Using Mahalanobis-Taguchi System and Clustering Algorithm

The devastating complexity of decision making in severe dynamic competitive environment of the universe, has forced the wise managers to have relevant strategic plans for their firms. In this paper, a new approach by utilizing Mahalanobis-Taguchi System (MTS) and clustering algorithm in formulating the strategy has been proposed. In this approach, first by performing environmental analysis all internal and external factors affecting organization will be listed. Then the long range goals will be identified by top managers. By applying MTS the main set of factors affecting goals will come out. By identifying main factors, the goal-factor matrix will be formed. At this stage, by using clustering algorithm the proper clusters containing goals and factors influencing them will be constructed. Finally, from the created clusters the appropriate strategies would be generated. The advantage of applying this method is its accuracy and ease of applications in the environment with plenty of goals and numerous factors with interactions among them.

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms;Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

Hierarchical Modeling by Recursive Unsupervised Spectral Clustering and Network Extended Importance Measures to Analyze the Reliability Characteristics of Complex Network Systems

The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.

Bayesian serial revision method for RLLC cluster systems failure prediction

Failure prediction plays an important role for many tasks such as optimal resource management in large-scale system. However, accurately failure number prediction of repairable large-scale long-running computing （RLLC） is a challenge because of the reparability and large-scale. To address the challenge, a general Bayesian serial revision prediction method based on Bootstrap approach and moving average approach is put forward, which can make an accurately prediction for the failure number. To demonstrate the performance gains of our method, extensive experiments on the data of Los Alamos National Laboratory （LANL） cluster is implemented, which is a typical RLLC system. And experimental results show that the prediction accuracy of our method is 80.2 %, and it is a greatly improvement with 4 % compared with some typical methods. Finally, the managerial implications of the models are discussed.

New Chiral Metal Cluster Systems for Catalytic AsymmetricSyntheses of Chiral Alcohols

The efficient chiral Ru 3(CO) 12 systems were prepared in situ from Ru 3(CO) 12 and various chiral diimino-or diamino-diphosphine tetradentate ligands. The systems have been used for the asymmetric transfer hydrogenation of propiophenone in 2-propanol, leading to 1-phenyl-1-propanol in a 98% yield and 96% e.e. The IR study suggests that the carbonyl hydride anion [HRu 3(CO) 11]- most probably exists as a principal species under the reaction conditions. The high chiral efficiency may be due to the synergetic effect produced by the neighboring ruthenium atoms and a special chiral micro-environment involving the polydentate ligand and the Ru 3 framework.

Fault Detection Based on Hierarchical Cluster Analysis in Wide Area Backup Protection System

In wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our study, global information will be introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluster analysis theory, we are using mainly hierarchical cluster analysis to search after the statistical laws of electrical quantities' marked changes. Then we carry out fast and exact detection of fault components and fault sections, and finally accomplish fault isolation. The facts show that the fault detection of fault component (fault section) can be performed successfully by hierarchical cluster analysis and calculation. The results of hierarchical cluster analysis are accurate and reliable, and the dendrograms of hierarchical cluster analysis are in intuition.

Parallel recovery method in shared-nothing spatial database cluster system

Shared nothing spatial database cluster system provides high availability since a replicated node can continue service even if any node in cluster system was crashed. However if the failed node wouldn’t be recovered quickly, whole system performance will decrease since the other nodes must process the queries which the failed node may be processed. Therefore the recovery of cluster system is very important to provide the stable service. In most previous proposed techniques, external logs should be recorded in all nodes even if the failed node does not exist. So update transactions are processed slowly. Also recovery time of the failed node increases since a single storage for all database is used to record external logs in each node. Therefore we propose a parallel recovery method for recovering the failed node quickly.

Theoretically Catalytic Synthesis of 5-Nitro-1,2,4-Triazol-3-One in Inert Gas Clustered System (X₆, X = He, Ne)

Inert gas-clustered systems (Xn, X = He, Ne, Ar and n = 2 - 20) were established in this study and their stability as a result of interparticulate interaction was examined. Ferric chloride and ferrous oxides were used as catalysts to promote reaction, and 5-nitro-1,2,4-triazol-3-one (NTO) was theoretically synthesized under an inert gas (X6)-clustered environment in this study. The raw material, urea, initially underwent chlorination using chlorine as the reagent, followed by amination, formylation and nitration. Reaction routes closely related to the experimental processes were successfully constructed, and the corresponding energy barriers were estimated for each elementary reaction. The findings revealed that the average errors in the B3LYP/6-31G(d, p)-calculated geometry and vibrational frequency of NTO in an Ne6 system relative to the observed values were 0.83% and 1.84%, respectively. The neon gas-clustered system achieved greater stabilization, which results from the difference in self-consistent field energy (ESCF), than the corresponding stabilization acquired in a helium- or argon-based system. Ferric chloride serves as a good catalyst to reduce the energy barrier of the chlorination reaction, and ferrous oxide is suitable for catalyzing the amination, formylation and nitration reactions, although nitric acid is the better agent for nitration. The catalytic Ne6-clustered reaction system is suggested to be a more feasible pathway for the synthesis of NTO.

Load-Balance Policy in Two Level-Cluster File System

In this paper, we explored a load-balancing algorithm in a cluster file system contains two levels of metadata-server, primary-level server quickly distributestasks to second-level servers depending on the closest load-balancing information. At the same time, we explored a method which accurately reflect I/O traffic and storage of storage-node： computing the heat-value of file, according to which we realized a more logical storage allocation. According to the experiment result, we conclude that this new algorithm shortens the executing time of tasks and improves the system performance compared with other load algorithm.

基于LMDI和系统聚类的电力行业碳排放影响因素分析

电力行业作为直接使用一次能源的最大部门,是落实我国碳减排目标的重点行业。为厘清电力行业碳排放的主要驱动或抑制来源,论文构建了LMDI模型,从国家及省域两个层面对2006—2020年电力行业碳排放的影响因素进行了分解。研究结果表明,从国家及省域两个层面来看,经济发展效应均为电力碳排放的主要促进因素,火电燃料转化效应和产业结构效应均为电力碳排放的抑制因素,电源结构效应、工业电耗强度效应在全国层面为电力碳排放的抑制因素,但在各省份中的影响效果及程度各有不同。论文以主要抑制因素为变量,利用系统聚类法将30个省份划分为六大区域,针对各区域影响因素的作用效果提出了因地制宜的减排政策。

Successive lag cluster consensus on multi-agent systems via delay-dependent impulsive control

We introduce a new consensus pattern, named a successive lag cluster consensus(SLCC), which is a generalized pattern of successive lag consensus(SLC). By applying delay-dependent impulsive control, the SLCC of first-order and second-order multi-agent systems is discussed. Furthermore, based on graph theory and stability theory, some sufficient conditions for the stability of SLCC on multi-agent systems are obtained. Finally, several numerical examples are given to verify the correctness of our theoretical results.

数字文化产业虚拟集群的形成机理及其系统动力学分析

因现代信息技术与互联网平台属性加持,数字文化产业虚拟集群相较于传统地理集群,存在更强的集群正外部性、规模经济效应、知识溢出效应和产业分工效应,即数字文化产业虚拟集群的形成存在发展规律与内在逻辑上的必然。基于系统动力学模型,发现良好的社会经济环境、数字基础设施、升级的文化消费需求、技术要素、人力资本、文化资源禀赋以及政策因素,在“经济—资源—政策”系统内形成了显著的正反馈作用,这也是数字文化产业虚拟集群最终形成的决定性动因。我国应继续鼓励并推动文化产业融合,积极拓展虚拟集群业务功能,努力完善数字文化内容版权保护机制,强化虚拟集群的平台治理水平,引导数字文化产业虚拟集群的价值导向,从而更好地释放集群的文化价值与经济影响。

中国典型城市交通出行特征及未来道路交通演变研究

为建立适合不同城市特点的交通体系,并明确定位汽车在其中承担的作用,文章首先从外部环境和交通工具两大维度出发,建立了表征城市整体交通出行情况的多层级评价体系;并基于一级指标的得分,对选取的9座中国典型城市的交通出行特征进行了聚类分析,确定了城市的不同类型;然后通过多元回归分析,识别了影响城市道路交通的主要因素;最后通过相关因素变化的情景假设,预测了各城市道路交通效率可能的演变情况,并提出了相关政策建议。

数字经济背景下智慧供应链发展水平测度——以长江经济带为例

物联网、云计算及大数据等技术的深度融合促进了数字经济的发展,也促进了区域智慧供应链的发展。长江经济带是连接东西部的重要区域,其智慧供应链的发展水平是判断其良性发展的重要依据。为此文章基于数字经济背景,从资源、输出和柔性三个维度构建了长江经济带智慧供应链发展水平评价体系,采用博弈论组合赋权法确定各指标权重,测算了长江经济带11省(市)2016年至2021年智慧供应链发展水平,运用系统聚类分析法和马尔科夫链统计分析方法探索了长江经济带智慧供应链的时空特征及未来发展趋势,并根据分析结果对其智慧供应链发展提出相应的建议。

新农科背景下全产业链复合应用型人才培养体系重构研究

培养适应产业需求、引领产业创新发展的高级专门人才,是新时代新农科建设的巨大挑战。以现代产业链的理念,高校应重构新农科全产业链复合应用型人才培养体系,打破传统学院建制,建设涉农专业集群和产业专业。农业高校基于产业需求,应充分发挥学校、企业和学生三方的主观能动性,开展校企深度融合,建立“产教研融合、院团社合一”的全产业链型人才培养模式,推动产业专业人才全面发展,解决高校人才输出与企业输入有效衔接问题。基于全产业链型的新农科人才培养模式,对于提升高校办学质量和促进产业全面发展,都具有十分重要的意义。

The Cluster Fe₂Si₁₈as the New Quantum Bit System

Multiconfiguration quantum chemical calculation of geometry and electron properties of Fe2Si18 cluster indicates on the predictable change of spin states as a function of the excitation energy beginning from ground state with the total spin S = 4. The charges on the two Fe atoms are quite different as well as the charge distribution on the surrounding Si atoms. Nevertheless the total dipole moment of the cluster is a monotonically decreasing function of the excitation energy and it reaches practically zero value in the first singlet state in which the cluster represents a new version of a quibit system.

Electron Spin Resonance(ESR)Spectroscopy of the Transition-metal Complexes and Clusters I.Systematic Investigations of Real-and Complex-Orbital Methods for Calculating the d-Orbital Energies of a Low-spin(S=1/2)nd^5(t_2~5,~2T_2)(n=3

On the basis of the first paper’s theoretical derivations and concrete instance calculations of the energies of the d orbitals for a low spin ( S =1/2) nd 5(t 2 5, 2T 2)(n =3, 4, 5) system, the major results reported in this paper contain the following two respects: explicit relationships between the coefficients of the real and complex Kramers doublets have been derived by using two types of the expressions of the principal components of the g tensors in real and complex orbital representations obtained in the first paper; the use of these relationships of the real and complex orbital coefficients has carried out a series of mathematical demonstrations on the agreement of the real and complex orbital methods .