Study on the Planning of Cluster-based Scenic Areas with Jingyanggang Scenic Area as an Example

Cluster-based scenic area is a special form within the scenic area system, scenic areas are always scattered with diversified landscape resources, thus planning should be made according to actual conditions of the local area, and pertinent measures should be applied. Through elaborating the detail work in the overall planning of Jingyanggang Scenic Area, such as landscape division, spatial layout and sightseeing structure, the authors discussed several problems should be paid more attention in the planning of cluster-based scenic areas.

Effective search with saliency-based matching and cluster-based browsing

Similarity matching and this paper, a saliency-based information presentation are two matching algorithm is proposed key factors in information retrieval. In for user-oriented search based on the psychological studies on human perception, and major emphasis on the saliently similar aspect of objects to be compared is placed and thus the search result is more agreeable for users. After relevant results are obtained, the cluster-based browsing algorithm is adopted for search result presentation based on social network analysis. By organizing the results in clustered lists, the user can have a general understanding of the whole collection by viewing only a small part of results and locate those of major interest rapidly. Experimental results demonstrate the advantages of the proposed algorithm over the traditional work.

Capacity Research in Cluster-Based Underwater Wireless Sensor Networks Based on Stochastic Geometry

Underwater wireless sensor networks(UWSNs) have attracted wide attention in recent years.The capacity research on it is still in the initial stage,lacking adequate performance evaluation for network construction.This paper will focus on this subject by theoretical analysis and simulation,aiming to provide some insights for the actual UWSNs construction.According to the structure features of cluster-based UWSNs and the propagation characteristics of underwater acoustic signal,with the combination of signal to interference plus noise ratio,we define some capacity performance metrics,such as outage probability and transmission capacity.Based on the theory of stochastic geometry,a network capacity analytical model used in the cluster-based UWSNs is presented.The simulation results verify the validity of the theoretical analysis,and the cause of error between theoretical and simulation results has also been clearly explained.

Cluster-Based Distributed Algorithms for Very Large Linear Equations

In many applications such as computational fluid dynamics and weather prediction, as well as image processing and state of Markov chain etc., the grade of matrix n is often very large, and any serial algorithm cannot solve the problems. A distributed cluster-based solution for very large linear equations is discussed, it includes the definitions of notations, partition of matrix, communication mechanism, and a master-slaver algorithm etc., the computing cost is O（n^3/N）, the memory cost is O（n^2/N）, the I/O cost is O（n^2/N）, and the com- munication cost is O（Nn ）, here, N is the number of computing nodes or processes. Some tests show that the solution could solve the double type of matrix under 10^6 × 10^6 effectively.

A Cluster-Based Random Key Revocation Protocol for Wireless Sensor Networks

In recent years,several random key pre-distribution schemes have been proposed to bootstrap keys for encryption,but the problem of key and node revocation has received relatively little attention.In this paper,based on a random key pre-distribution scheme using clustering,we present a novel random key revoca-tion protocol,which is suitable for large scale networks greatly and removes compromised information efficiently.The revocation protocol can guarantee network security by using less memory consumption and communication load,and combined by centralized and distributed revoca-tion,having virtues of timeliness and veracity for revoca-tion at the same time.

Pre-Emptive Traffic Management for a Cluster-Based TDMA System in Vehicular Communications

The IEEE 802.11p is a standard in a vehicular communication system, known as Wireless Access in Vehicular Environment (WAVE). An implementation of that standard as the MAC Protocol in a high-density of nodes in Vehicular Ad-Hoc Networks (VANETs) may create a performance drawback, in particular for packet loss and delay whenever collisions happen. Introducing Time Division Multiple Access (TDMA) schemes can improve the performance. However, TDMA scheduling is difficult to manage the case of high-density of traffic, the high mobility of vehicles, and dynamic network topology. This journal proposes a clustered-based TDMA by traffic priority in VANETs. The clustered traffic is defined as high and low traffic priority and embedded in TDMA MAC Header. The evaluation result obtained through NS3 Simulator shows that the proposed approach performed better in a high-density of nodes.

Optimal Positions of Relay Stations for Cluster-Based Two-Hop Cellular Network

Multihop cellular networks is an exciting and a fledgling area of wireless communication which offers huge potential in terms of coverage enhancement, data-rates, power reduction, and various other quality of service improvements. However, resource allocation in MCN is an NP-hard problem. Hence, significant research needs to be done in this field in order to efficiently design the radio network. In this paper, optimal position of relay stations in a hierarchical cluster-based two-hop cellular network is investigated. Vector algebra has been used to derive general equation for carrier-to-interference ratio (C/I) of a mobile station. It has been observed that when the transmit power of base station (BS) and the gateway (GTW)/relay station (RS) are same, the RSs should be located close to mid-point of BS and the edge of the cell. However, significantly, when the transmit power of the BS is greater than that of the GTW, then the RSs should be placed closer to the edge of the cell, in order to maximize the minimum C/I at any point in the cell. This in turn results in higher modulation technique at the physical layer, and hence, a higher data-rate to all the users in the system.

Cluster-Based Design for Two-hop Cellular Networks

Optimal resource allocation with an objective of maximizing the system capacity is an NP-hard problem in multihop cellular networks. Hence, different heuristic algorithms have been developed over the years that would improve the network system capacity. In this paper, a novel cluster-based architecture is proposed for a two-hop cellular network whereby the transmission distance between any communicating pair is restricted to half the cell radius. In this design, a given radio resource is used by two simultaneously communicating pairs in every hexagonal cell, but for only half the time slot period. The characteristic feature of this cluster-based design is that it enables a frequency reuse ratio of one. The proposed hierarchical system is analyzed and tested under realistic propagation conditions including lognormal shadowing. It has been observed that the system capacity of a cluster-based design is 2.5 times that obtained from the single-hop cellular system with no relaying. In addition, the cluster-based design achieves higher capacity compared to state-of-the-art two-hop algorithms. This is an important finding since the hierarchical cluster-based approach has fewer degrees of freedom in the selection of the routing path for the end-to-end connection. Practical routing algorithms should be able to benefit from this.

Strengthening the Competitiveness of SMEs by Using the Cluster-Based Approach： A Case Study of the Ratchaburi Orchid Cluster in Thailand

This paper studies the application of the cluster-based approach in the enhancement of the competitiveness of Thailand＇s SME industry. The author had employed a qualitative method through the in-depth interview. The result showed that Ratchaburi orchid cluster in Thailand has employed the concept of the cluster-based approach since they realized that it was useful and could enable them to produce good quality orchids for the international market. The finding also showed how individuals have worked together and helped each other, in order to build a good horizontal network of support and creating competitive advantages. In addition, the research paper related to knowledge management because knowledge management refers to a method for development which requires cluster members to exchange information, interact with each other, sharing and distribute information, create closer business relationship and build mutual benefit. Therefore, the cluster members will help each other to create a culture that values learning through making a commitment and sharing information to strengthen the cluster.

Early identification of scientific breakthroughs through outlier analysis based on research entities

Purpose:To address the“anomalies”that occur when scientific breakthroughs emerge,this study focuses on identifying early signs and nascent stages of breakthrough innovations from the perspective of outliers,aiming to achieve early identification of scientific breakthroughs in papers.Design/methodology/approach:This study utilizes semantic technology to extract research entities from the titles and abstracts of papers to represent each paper’s research content.Outlier detection methods are then employed to measure and analyze the anomalies in breakthrough papers during their early stages.The development and evolution process are traced using literature time tags.Finally,a case study is conducted using the key publications of the 2021 Nobel Prize laureates in Physiology or Medicine.Findings:Through manual analysis of all identified outlier papers,the effectiveness of the proposed method for early identifying potential scientific breakthroughs is verified.Research limitations:The study’s applicability has only been empirically tested in the biomedical field.More data from various fields are needed to validate the robustness and generalizability of the method.Practical implications:This study provides a valuable supplement to current methods for early identification of scientific breakthroughs,effectively supporting technological intelligence decision-making and services.Originality/value:The study introduces a novel approach to early identification of scientific breakthroughs by leveraging outlier analysis of research entities,offering a more sensitive,precise,and fine-grained alternative method compared to traditional citation-based evaluations,which enhances the ability to identify nascent breakthrough innovations.

A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular health.Collectively,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)techniques.These findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning approaches.This work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization methodologies.Future studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.

Changepoint Detection with Outliers Based on RWPCA

Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.

基于快速SVDD的无线传感器网络Outlier检测

Outlier是基于无线传感器网络的数据收集应用中常见的数据故障类型,严重影响数据质量。本文提出一种基于快速SVDD的无线传感器网络Outlier检测方法,其基本思想是:首先利用快速SVDD算法获得包含正常样本的最小球形边界,然后通过该边界判断未知样本的类别,本法采用训练集约减策略和基于二阶逼近的SMO算法来加速SVDD的训练。基于合成数据和真实数据的仿真实验表明,该方法在确保分类精度的同时,运行速度快,内存开销小,适用于资源有限的无线传感器网络。

η-one-class问题和η-outlier及其LP学习算法

用SVM方法研究one class和outlier问题 .在将one class问题理解为一种函数估计问题的基础上 ,作者首次定义了 η one class和 η outlier问题的泛化错误 ,进而定义了线性可分性和边缘 ,得到了求解one class问题的最大边缘、软边缘和ν 软边缘算法 .这些学习算法具有统计学习理论依据并可归结为求解线性规划问题 .算法的实现采用与boosting类似的思路 .实验结果表明该文的算法是有实际意义的 .

Outlier-DivideConquer:近似聚集查询中离群分治取样算法

取样是一种通用有效的近似技术,利用取样技术进行近似聚集查询处理是决策支持系统和数据挖掘实现技术中的常用方法.如何正确有效地给出近似查询结果并最小化近似查询误差是近似查询处理的关键和目标.在深入研究近似聚集查询取样方法的基础上,本文提出了一个有误差确界且只需单遍扫描数据集的离群分治取样Outlier-DivideConquer算法,该算法在聚集属性内部存在高方差分布时能克服随机均匀取样局限,可显著降低近似查询误差,且执行效率优于同类算法.最后通过与传统均匀取样算法的实验比较验证了Outlier-DivideConquer算法的有效性和正确性.

一类结合Outlier分析的单变量ARIMA模型在股票市场中的应用

本文通过对当前广泛使用的经济时间序列预测方法的分析比较，针对如股票价格这一类易受到大量外部因素影响且难以通过多变量建模分析的经济现象，采用了单变量ＡＲＩＭＡ模型并结合Ｏｕｔｌｉｅｒ分析的方法。

来自于Multiple-Outlier模型的最小次序统计量序性质（英文）

本文中,我们研究来自于两个multiple-outlier模型的最小次序统计量的随机比较,其中两个模型中独立同分布的随机变量个数不同.令X_(1:n)(p,q)和X_(1:n~*)(p~*,q~*)分别表示来自于X_1,…,X_p,X_(p+1),…,X_n和X_1,…,X_(p),X_(p~*+1),…,X_(n)的最小次序统计量,这里q=n-p,q~*=n~*-p~*.在参数(p,q)和(p~*,q~*)满足某些优化序条件下,我们根据普通随机序,失效率序和似然比序给出了X_(1:n)(p,q)和X_(1:n~*)(p~*,q~*)的序比较.

Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network

Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.

Density-based trajectory outlier detection algorithm

With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.

GA-iForest: An Efficient Isolated Forest Framework Based on Genetic Algorithm for Numerical Data Outlier Detection

With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,outlier detection is related to the quality of data.The isolated forest algorithm is one of the more prominent numerical data outlier detection algorithms in recent years.In the process of constructing the isolation tree by the isolated forest algorithm,as the isolation tree is continuously generated,the difference of isolation trees will gradually decrease or even no difference,which will result in the waste of memory and reduced efficiency of outlier detection.And in the constructed isolation trees,some isolation trees cannot detect outlier.In this paper,an improved iForest-based method GA-iForest is proposed.This method optimizes the isolated forest by selecting some better isolation trees according to the detection accuracy and the difference of isolation trees,thereby reducing some duplicate,similar and poor detection isolation trees and improving the accuracy and stability of outlier detection.In the experiment,Ubuntu system and Spark platform are used to build the experiment environment.The outlier datasets provided by ODDS are used as test.According to indicators such as the accuracy,recall rate,ROC curves,AUC and execution time,the performance of the proposed method is evaluated.Experimental results show that the proposed method can not only improve the accuracy and stability of outlier detection,but also reduce the number of isolation trees by 20%-40%compared with the original iForest method.