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Getmanets, O., A. Nekos, and M. Pelikhatyi. "CLUSTER ANALYSIS AND RADIATION MONITORING OF ENVIRONMENT." Visnyk of Taras Shevchenko National University of Kyiv. Geology, no. 3 (86) (2019): 75–79. http://dx.doi.org/10.17721/1728-2713.86.11.
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Building a background radiation field on the ground on the basis of measurement data taken at a finite number of points is one of the most important tasks of radiation monitoring. The aim of the work: to study the possibility of applying cluster analysis for the tasks of radiation monitoring of the environment. Cluster analysis is a multidimensional statistical analysis. Its main purpose is to split the set of objects under study (observation points) into homogeneous groups or clusters, that is, the task of classifying data and identifying the corresponding structure in them is solved. Methods of research: the measurements of the power of the ambient dose of continuous X-ray and gamma radiation on the terrain by using the MKS-05 dosimeter "TERRA-0"; processing of the obtained data by cluster analysis methods using the computer program "Statistics-10", wherein each cluster point is characterized by three coordinates: two coordinates on the ground and the power of the ambient dose of radiation at a given point; Euclidean distance was chosen as the distance between two points. Results: after processing data using various clustering methods: the method of Complete Linkage, the method of Weighted pair-group average and the Ward's method, it was found that the results of the analysis practically coincide with each other, that proves the reliability of the application of cluster analysis for the tasks of radiation monitoring of the environment and mapping of radiation pollution. Conclusions: the concept of a "radiation cluster" was first formulated in this work, combining coordinates on a plane with an ambient dose rate;the possibility of using cluster analysis to construct a map of radiation pollution of the environment has been proved by sequential projectionfrom more connected to less connected radiation clusters onto the plane of the controlled zone. In this sense, cluster analysis is similar to the operator approach to the construction of the radiation field. For further research, it is of some interest to study the issues of integration of cluster analysis with geographic information systems.
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Crooks, David, Mark Mitchell, Stuart Purdie, Gareth Roy, Samuel Cadellin Skipsey, and David Britton. "Monitoring in a grid cluster." Journal of Physics: Conference Series 513, no. 6 (June 11, 2014): 062010. http://dx.doi.org/10.1088/1742-6596/513/6/062010.
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Nazarov, Alexey N. "Processing streams in a monitoring cloud cluster." Russian Technological Journal 7, no. 6 (January 10, 2020): 56–67. http://dx.doi.org/10.32362/2500-316x-2019-7-6-56-67.
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The creation of monitoring clusters based on cloud computing technologies is a promising direction for the development of systems for continuous monitoring of objects for various purposes in the web space. Hadoop web-programming environment is the technological basis for the development of algorithmic and software solutions for the synthesis of monitoring clusters, including information security and information counteraction systems. The International Telecommunication Union’ (ITU) recommendations Y. 3510 present the requirements for cloud infrastructure that require monitoring the performance of deployed applications based on the collection of real-world statistics. Often, computing resources of monitoring clusters of cloud data centers are allocated for continuous parallel processing of high-speed streaming data, which imposes new requirements to monitoring technologies, necessitating the creation and research of new models of parallel computing. The need to use service monitoring plays an important role in the cloud computing industry, especially for SLA/QoS assessment, as the application or service may experience problems even if the virtual machines on which the work is taking place appear to be operational. This requires to study the methodological possibilities of organization to study of parallel processing high-speed streaming services with the processing of huge amounts of bit data, and, simultaneously, to estimate the necessary computational resource. In the conditions of high dynamics of changes in the bit rate of information generation from the source, a model of the bit rate of Discretized Stream (DStream) formation is proposed, which has a common application. Based on the poly-burst nature of the bit rate model, a model of group content traffic of any sources of different services processed in the cloud cluster was created. The obtained results made it possible to develop mathematical models of parallel DStreams from sources processed in a cloud cluster via Hadoop technology using the micro-batch architecture of the Spark Streaming module. These models take into account the flow of requests for maintenance from sources of different services, on the one hand, and, on the other hand, the needs of services in bit rate, taking into account the multichannel traffic of sources of various services. At the same time, analytical relations are obtained to calculate the required performance of the Hadoop cluster at a given value of the probability of batch loss.
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Terry, L. Irene, and Gloria DeGrandi-Hoffman. "MONITORING WESTERN FLOWER THRIPS (THYSANOPTERA: THRIPIDAE) IN “GRANNY SMITH” APPLE BLOSSOM CLUSTERS." Canadian Entomologist 120, no. 11 (November 1988): 1003–16. http://dx.doi.org/10.4039/ent1201003-11.
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AbstractThe efficiency and accuracy of sampling western flower thrips (Frankliniella occidentalis [Pergande]) from “Granny Smith” apple blossom clusters were analyzed during 1986–1987 to develop a sampling plan for research purposes. The accuracy of the “shake” method was compared with an “extraction” process of each of three blossom cluster types: pink, open, and petalless (petal fall). Thrip extractions from combined clusters revealed that a 9-s and 6-s “shake” removed 84 and 74%, of the thrips, respectively, but a 3-s “shake” removed 53%, and was more variable. Open blossom clusters always had higher thrips densities than either pink or petal fall clusters, regardless of the bloom state. The effects of cardinal position within trees were not consistent over time. Clusters from the top of the canopy had more thrips than lower canopy clusters, and apical clusters had more thrips than basal clusters during peak bloom. Variance component analyses indicated that thrips counts from clusters within tree were more variable than counts among trees, even when cluster types were analyzed separately. Two sets of indices (Iwao’s regression of mean crowding on mean density and Taylor’s regression of log variance on log mean density) for each cluster type indicated aggregated spatial patterns. Precision level sampling plans were developed using Iwao’s regression coefficients.
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Borisov, Vadim, Maksim Dli, Artem Vasiliev, Yaroslav Fedulov, Elena Kirillova, and Nikolay Kulyasov. "Energy System Monitoring Based on Fuzzy Cognitive Modeling and Dynamic Clustering." Energies 14, no. 18 (September 15, 2021): 5848. http://dx.doi.org/10.3390/en14185848.
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A feature of energy systems (ESs) is the diversity of objects, as well as the variety and manifold of the interconnections between them. A method for monitoring ESs clusters is proposed based on the combined use of a fuzzy cognitive approach and dynamic clustering. A fuzzy cognitive approach allows one to represent the interdependencies between ESs objects in the form of fuzzy impact relations, the analysis results of which are used to substantiate indicators for fuzzy clustering of ESs objects and to analyze the stability of clusters and ESs. Dynamic clustering methods are used to monitor the cluster structure of ESs, namely, to assess the drift of cluster centers, to determine the disappearance or emergence of new clusters, and to unite or separate clusters of ESs.
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Marino, Stefano, and Arturo Alvino. "Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits." Remote Sensing 13, no. 4 (February 3, 2021): 541. http://dx.doi.org/10.3390/rs13040541.
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Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha−1 for the first cluster, 4.22 t ha−1 for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha−1 for cluster 1 and 4.74, 4.31, and 4.66 t ha−1 for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha−1), followed by cluster 2 (5.6 t ha−1), cluster 3 (4.31 t ha−1), and cluster 4 (3.85 t ha−1). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops.
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ВЫЖИТОВИЧ, Александр, Aleksandr VYZHITOVICH, Олег ЛЯМЗИН, and Oleg LYAMZIN. "MODELING THE MONITORING SERVICE FUNCTION IN A MEMBER ORGANIZATION OF A CLUSTER STRUCTURE." Services in Russia and abroad 11, no. 4 (July 4, 2017): 44–54. http://dx.doi.org/10.22412/1995-042x-11-4-4.
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The article is devoted to the issues of improving the enterprise monitoring services in the context of its inclusion in the cluster structure. One of the most promising areas for increasing the activity of territories is the functioning of clusters in various sectors of the economy. Against the backdrop of the activity on their creation and state support, the issue of the significance of the control service regarding the work of enterprises as participants in clusters, in the economic literature is insufficiently disclosed. The problem of organizing an adequate internal control at the enterprise as part of the overall problem of the forming its effective appraisal and analytical system remains largely unresolved.
The article is focused on the organization activity when forming integration cluster interactions, namely approaches and tools for modeling the function of the internal control service of an organization when joining a cluster.
The purpose of the research is to test the possibility of modeling the process of qualitative expertise in the implementation of the cluster initiative and develop, on this basis, proposals for the organization of internal control of an enterprise as a participant in the cluster. This is supposed to be done through the business game "Internal Audit in a Cluster" based on the assignment of a conditional Board of Directors, where the task of forming an expert opinion on the need to implement measures for an enterprise as a member of a cluster should be decided for the internal audit unit.
The authors offer an approach for monitoring the state of internal control based on the game method "traffic light". Game experiment allows to improve the expertise quality of proposals for participation in the cluster and to form the directions of work in the internal control system.
The application of a business game with the participation of professional practitioners makes it possible to test a new management technology for identifying and assessing enterprise risks in cluster projects, to improve the quality of control as a service function for enterprise management bodies.
The research results can be used by the participants of clusters created and planned to be created for building and improving their internal control system, conducting strategic sessions, developing cluster’s internal documents.
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Markov, L. S., V. B. Kurmashev, and A. F. Buruk. "Peculiarities of Regional Cluster Policy Monitoring." World of Economics and Management 18, no. 3 (2018): 91–103. http://dx.doi.org/10.25205/2542-0429-2018-18-3-91-103.
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Fang, Kun, Chengyin Liu, and Jun Teng. "Cluster-based optimal wireless sensor deployment for structural health monitoring." Structural Health Monitoring 17, no. 2 (February 3, 2017): 266–78. http://dx.doi.org/10.1177/1475921717689967.
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A well-designed wireless sensor deployment method not only directly influences the number of deployed sensors and data accuracy, but also influences on network topology. As most of the energy cost comes from the transmission and receiving of data packets, clustering optimization in wireless sensor network becomes an important issue for energy-efficient coordination among the densely deployed nodes for data communication. In a typical hierarchical wireless sensor network, total intra-cluster communication distance and total distance of cluster heads to base station depend on number of cluster heads. This work presents a novel approach by selecting the number of clusters in hierarchical wireless sensor network. We analyze and demonstrate the validity of the cluster optimization for wireless sensor deployment using an example of a numerically simulated simply supported truss, in terms of efficient use of the constrained wireless sensor network resources. Followed by a cluster-based optimization framework, we show how to adopt our approach to achieve scalable and efficient deployment, through a comprehensive optimization study of a realistic wireless structural health monitoring system. Finally, we suggest optimal deployment scheme based on the comparative performance evaluation results in the case study.
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Trachenko, M. B., and O. D. Gaisha. "Evaluation of the Effectiveness of Financing Industrial Clusters." Russian Economic Journal, no. 5 (November 2019): 36–47. http://dx.doi.org/10.33983/0130-9757-2019-5-36-47.
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The article is solving an actual problem — development of a system of indicators to evaluate the effectiveness of financing industrial clusters in Russia. The article analyzes the cluster models of Russian and foreign authors, identifies their strengths and weaknesses. A universal information model of the cluster was developed, reflecting the interaction of the participants among themselves and with external stakeholders of the cluster development. The developed model has three control loops: internal cluster stakeholders, cluster, cluster's region. Each has the specificity of the movement of inventory and cash flows, information interaction in the implementation of cluster policy, and reflects the interests of various stakeholders of industrial clusters. The model lays the groundwork to justify a three-tier system of indicators to evaluate the effectiveness of financing industrial clusters. The subsystems of the indicators of the impact of the industrial cluster on the regional economy, of the indicators of the industrial cluster development and the subsystem of the indicators of the financial condition of enterprises participating in the industrial cluster are highlighted in the proposed system. The study used the methods of bibliographic and logical analysis, synthesis and systems approach, mathematical methods of statistical data processing. The developed system of indicators for assessing the effectiveness of financing industrial clusters can be used to conduct current and subsequent monitoring of financing the implementation of cluster programs, to prepare decisions on the allocation of budgetary funds by state and municipal authorities, and to potential investors to determine the most promising investment instruments.
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Герасимов, Кирилл, Kirill Gerasimov, Николай Просвиркин, and Nikolay Prosvirkin. "MANAGEMENT INTERACTION OF ENTERPRISES IN INDUSTRIAL CLUSTERS." Russian Journal of Management 4, no. 2 (June 25, 2016): 218–23. http://dx.doi.org/10.12737/19854.
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The article is devoted to the development of methodological tools to address the problem of existence of the negative aspects of clustering. For this purpose, a special author’s technique and the system for monitoring the effectiveness of interaction between enterprises as part of the industrial cluster are developed. The advantages of the developed control system areits ease of use, speed of obtaining results of the evaluation, the availability of source data for the calculations, and the ability to use it not only for existing clusters, but also for projected clusters, which helps prevent the possible negative aspects of clustering. The authors also performed testing of the developed system for monitoring the effectiveness of interaction between enterprises within an existing industrial cluster as an example of the Volga Automobile Cluster of Russia and the planned metallurgical cluster in Russia.
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Rosly, Sitti Sufiah Atirah, Balkiah Moktar, and Muhamad Hasbullah Mohd Razali. "Comparison of Clustering Algorithms on Air Quality Substances in Peninsular Malaysia." Journal of Computing Research and Innovation 2, no. 1 (January 26, 2018): 36–44. http://dx.doi.org/10.24191/jcrinn.v2i1.28.
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Air quality is one of the most popular environmental problems in this globalization era. Air pollution is the poisonous air that comes from car emissions, smog, open burning, chemicals from factories and other particles and gases. This harmful air can give adverse effects to human health and the environment. In order to provide information which areas are better for the residents in Malaysia, cluster analysis is used to determine the areas that can be clustering together based on their a ir quality through several air quality substances. Monthly data from 37 monitoring stations in Peninsular Malaysia from the year 2013 to 2015 were used in this study. K - Means (KM) clustering algorithm, Expectation Maximization (EM) clustering algorithm and Density Based (DB) clustering algorithm have been chosen as the techniques to analyze the cluster analysis by utilizing the Waikato Environment for Knowledge Analysis (WEKA) tools. Results show that K - means clustering algorithm is the best method among ot her algorithms due to its simplicity and time taken to build the model. The output of K - means clustering algorithm shows that it can cluster the area into two clusters, namely as cluster 0 and cluster 1. Clusters 0 consist of 16 monitoring stations and clu ster 1 consists of 36 monitoring stations in Peninsular Malaysia.
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Haiahem, Rahim, Pascale Minet, Selma Boumerdassi, and Leila Azouz Saidane. "Collision-Free Transmissions in an IoT Monitoring Application Based on LoRaWAN." Sensors 20, no. 14 (July 21, 2020): 4053. http://dx.doi.org/10.3390/s20144053.
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With the Internet of Things (IoT), the number of monitoring applications deployed is considerably increasing, whatever the field considered: smart city, smart agriculture, environment monitoring, air pollution monitoring, to name a few. The LoRaWAN (Long Range Wide Area Network)architecture with its long range communication, its robustness to interference and its reduced energy consumption is an excellent candidate to support such applications. However, if the number of end devices is high, the reliability of LoRaWAN, measured by the Packet Delivery Ratio (PDR), becomes unacceptable due to an excessive number of collisions. In this paper, we propose two different families of solutions ensuring collision-free transmissions. The first family is TDMA (Time-Division Multiple Access)-based. All clusters transmit in sequence and up to six end devices with different spreading factors belonging to the same cluster are allowed to transmit in parallel. The second family is FDMA (Frequency Divsion Multiple Access)-based. All clusters transmit in parallel, each cluster on its own frequency. Within each cluster, all end devices transmit in sequence. Their performance are compared in terms of PDR, energy consumption by end device and maximum number of end devices supported. Simulation results corroborate the theoretical results and show the high efficiency of the solutions proposed.
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Saiz, Pablo, and Ulrich Schwickerath. "Large Elasticsearch cluster management." EPJ Web of Conferences 245 (2020): 07021. http://dx.doi.org/10.1051/epjconf/202024507021.
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The Centralised Elasticsearch Service at CERN runs the infrastructure to provide Elasticsearch clusters for more than 100 different use cases. This contribution presents how the infrastructure is managed, covering the resource distribution, instance creation, cluster monitoring and user support. The contribution will present the components that have been identified as critical in order to share resources and minimise the amount of clusters and machines needed to run the service. In particular, all the automation for the instance configuration, including index template management, backups and visualisation settings, will be explained in detail.
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Shohreh, Honarbakhsh, Zamani Mazdak, and Honarbakhsh Roza. "Dynamic Monitoring in Ad Hoc Network." Applied Mechanics and Materials 229-231 (November 2012): 1481–86. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1481.
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This paper, present a novel model to resolve the problems of evaluating trust value of nodes, and prepare a dynamic supervising in ad hoc network as well. Trust evaluation is not only based on direct interactions among nodes, but also observing previous behaviors of nodes, which come from all interactions through the network. This model specially decreases the delay time of selecting a new head for each cluster, which leads to prepare a dynamic monitoring in intra cluster and inter cluster in ad hoc network.
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Islam, Nazmul, Saurabh Dey, and Srinivas Sampalli. "Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs)." Sensors 18, no. 12 (December 4, 2018): 4258. http://dx.doi.org/10.3390/s18124258.
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Wireless Sensor Networks (WSNs) have become a significant part of surveillance techniques. With unequal clustering approaches and multi-hop communication, WSNs can balance energy among the clusters and serve a wide monitoring area. Recent research has shown significant improvements in unequal clustering approaches by forming clusters prior to the selection of cluster heads. These improvements adopt different geometric fractals, such as the Sierpinski triangle, to divide the monitoring area into multiple clusters. However, performance of such approaches can be improved further by cognitive partitioning of the monitoring area instead of adopting random fractals. This paper proposes a novel clustering approach that partitions the monitoring area in a cognitive way for balancing the energy consumption. In addition, the proposed approach adopts a two-layered scrutinization process for the selection of cluster heads that ensures minimum energy consumption from the network. Furthermore, it reduces the blind spot problem that escalates once the nodes start dying. The proposed approach has been tested in terms of number of alive nodes per round, energy consumption of nodes and clusters, and distribution of alive nodes in the network. Results show a significant improvement in balancing the energy consumption among clusters and a reduction in the blind spot problem.
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Lu, Chengchao, and Zhongjie Wang. "A cluster-autonomous partitioning algorithm in electrical power grid using Monte Carlo simulation." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 01 (January 23, 2018): 1750053. http://dx.doi.org/10.1142/s1793962317500532.
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Power grid partitioning decomposes a large power grid into several clusters. Most of the existing partitioning methods suffer from a limitation that the buses within a cluster are severely topologically disconnected after partitioning in some cases. As a result, a cluster will inevitably be assigned to two or more power grid corporations. This assignment obstructs inner-cluster monitoring and control applications of the transmission system. To overcome the limitation, this paper proposes a multi-index power grid partitioning approach using Monte Carlo simulation guaranteeing cluster connectivity to ensure the cluster autonomy. A line-based binary coding technique is developed to ensure the cluster connectivity. Three partitioning indices are considered: the coherency, the cluster connectivity, and the number of clusters. Finally, the proposed partitioning method is applied to IEEE 9-bus system, IEEE 39-bus system and IEEE 145-bus system and compared with Fuzzy C-medoid (FCMdd) algorithm.
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Langet, Nadège, Bettina Goertz-Allmann, Volker Oye, Robert A. Bauer, Sherilyn Williams-Stroud, Anna Maria Dichiarante, and Sallie E. Greenberg. "Joint Focal Mechanism Inversion Using Downhole and Surface Monitoring at the Decatur, Illinois, CO2 Injection Site." Bulletin of the Seismological Society of America 110, no. 5 (July 14, 2020): 2168–87. http://dx.doi.org/10.1785/0120200075.
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ABSTRACT The three-year CO2 injection period at the Illinois Basin - Decatur Project site (Decatur, Illinois, United States) produced a number of microseismic events distributed in very distinct spatiotemporal clusters with different orientations. Further characterization of the microseismicity encompasses the determination of the event source mechanisms. Initially, the microseismic monitoring network consisted solely of borehole sensors, but has been extended with surface sensors, thereby significantly improving the data coverage over the focal sphere. This article focuses on 23 events from the northernmost microseismic cluster (about 2 km from the injection point) and takes advantage of both, surface and downhole, recordings. The resulting strike-slip east–west-oriented focal planes are all consistent with the east–west orientation of the cluster in map view. The injection-related increase of pore pressure is far below the formation fracture pressure; however, small stress-field changes associated with the pore-pressure increase may reach as far as to the investigated cluster location. Monte Carlo modeling of the slip reactivation potential within this cluster showed that the observed maximum stress-field orientation of N068° is the optimum orientation for fault reactivation of the east–west-oriented cluster. Our results suggest that the east–west orientation of the investigated cluster is the main reason for its activation, even though the cluster is about 2 km away from the low-pressure injection point.
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Mackiewicz, Marta. "Role of clusters in the Polish innovation system." Central European Economic Journal 6, no. 53 (July 8, 2020): 304–10. http://dx.doi.org/10.2478/ceej-2019-0021.
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AbstractThis article presents the role of clusters in the Polish innovation system. This role has evolved in recent years due to maturing of cluster organisations and the expansion of their ability not just to provide services for cluster members but also to perform selected public tasks. This study aims to provide a better understanding of the nature and extent to which clusters can contribute to the objectives of development policies and thus to the economic development of the Polish economy and answer the question what role clusters can play in the innovation system. Based on a survey of 44 cluster organisations in Poland and interviews with cluster managers, the study explores the possibility of engaging Polish cluster organisations in the implementation of public policies. The results confirm that many of the Polish clusters achieved such a level of development that they themselves see the possibility of engaging in public tasks, for example education and specialised training, helping enterprises in digital transformation, monitoring technological trends, and so on. Therefore, it is justified pursuing a dual cluster policy. This duality means focus on two objectives: supporting cluster organisations on the one hand and implementing cluster-based development policies on the other hand.
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Kafka, S., D. G. Gibbs II, A. A. Henden, and R. K. Honeycutt. "WIYN Open Cluster Study. XX. Photometric Monitoring of the Galactic Cluster NGC 6939." Astronomical Journal 127, no. 3 (March 2004): 1622–31. http://dx.doi.org/10.1086/382102.
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Zhou, Guang-Dong, Ting-Hua Yi, and Hong-Nan Li. "Sensor Placement Optimization in Structural Health Monitoring Using Cluster-In-Cluster Firefly Algorithm." Advances in Structural Engineering 17, no. 8 (August 2014): 1103–15. http://dx.doi.org/10.1260/1369-4332.17.8.1103.
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Zhang, Jian Wei, Cheng Zhang, Ren Zong Ye, and Zi Xiang Zhong. "Research on Time Management for Project Cluster Development." Advanced Materials Research 779-780 (September 2013): 1758–61. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.1758.
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Project Cluster development usually reflects a large scale construction, the mutual connections of the project working programs, complex constitutions. This paper tries to analyses the difficulties of project clusters progress control and study some processes and methods of project cluster time management, through a series of measures of making the progress plan, schedule monitoring, adjustment and coordination in order to guarantee the project progress goals realization and internal resources coordination and ensure time management operates smoothly of a company.Keywords: Project Cluster; Method Study; Schedule Control; Resource Optimization; Time Management
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Ligare, Marshall R., Grant E. Johnson, and Julia Laskin. "Observing the real time formation of phosphine-ligated gold clusters by electrospray ionization mass spectrometry." Physical Chemistry Chemical Physics 19, no. 26 (2017): 17187–98. http://dx.doi.org/10.1039/c7cp01402c.
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S. Shraddha Bollamma, K., S. Manishankar, and M. V. Vishnu. "Optimizing the performance of hadoop clusters through efficient cluster management techniques." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 19. http://dx.doi.org/10.14419/ijet.v7i2.31.13389.
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The necessity for processing the huge data has become a critical task in the age of Internet, even though data processing has evolved into a next generation level still data processing and information extraction has many problems to solve. With the increase in data size retrieving useful information with a given span of time is a herculean task. The most optimal solution that has been adopted is usage of distributed computing environment supporting data processing involving suitable model architecture with large complex structure. Although processing has achieved good amount of improvement, efficiency, energy utilization and accuracy has been compromised. The research aims to propose an efficient environment for data processing with optimized energy utilization and increased performance. Hadoop environment common and popular among big data processing platform has been chosen as base for enhancement. Creating a multi node Hadoop cluster architecture on top of which an efficient cluster monitor is setup and an algorithm to manage efficiency of the cluster is formulated. Cluster monitor is incorporated with Zoo keeper, Yarn (Node and resource manager). Zoo keeper does the monitoring of cluster nodes of the distributed system and identifies critical performance problems. Yarn plays a vital role in managing the resources efficiently and controlling the nodes with the help of hybrid scheduler algorithm. Thus this integrated platform helps in monitoring the distributed cluster as well as improving the performance of the overall Big Data processing.
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Lee, Seulbi, Jaehoon Kim, Jongyeon Hwang, EunJi Lee, Kyoung-Jin Lee, Jeongkyu Oh, Jungsu Park, and Tae-Young Heo. "Clustering of Time Series Water Quality Data Using Dynamic Time Warping: A Case Study from the Bukhan River Water Quality Monitoring Network." Water 12, no. 9 (August 27, 2020): 2411. http://dx.doi.org/10.3390/w12092411.
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It is essential to monitor water quality for river water management because river water is used for various purposes and is directly related to the health and safety of a population. Proper network installation and removal is an important part of water quality monitoring and network operation efficiency. To do this, cluster analysis based on calculated similarity between measuring stations can be used. In this study, we measured the similarities between 12 water quality monitoring stations of the Bukhan River. River water quality data always have a station-dependent time lag because water flows from upstream to downstream; therefore, we proposed a Dynamic Time Warping (DTW) algorithm that searches for the minimum distance by changing and comparing time-points, rather than using the Euclidean algorithm, which compares the same time-point. Both Euclidean and DTW algorithms were applied to nine water quality variables to identify similarities between stations, and K-medoids cluster analysis were performed based on the similarity. The Clustering Validation Index (CVI) was used to select the optimal number of clusters. Our results show that the Euclidean algorithm formed clusters by mixing mainstream and tributary stations; the mainstream stations were largely divided into three different clusters. In contrast, the DTW algorithm formed clear clusters by reflecting the characteristics of water quality and watershed. Furthermore, because the Euclidean algorithm requires the lengths of the time series to be the same, data loss was inevitable. As a result, even where clusters were the same as those obtained by DTW, the characteristics of the water quality variables in the cluster differed. The DTW analysis in this study provides useful information for understanding the similarity or difference in water parameter values between different locations. Thus, the number and location of required monitoring stations can be adjusted to improve the efficiency of field monitoring network management.
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Gaisha, O. D. "MONITORING THE EFFECTIVENESS OF FINANCING A CLUSTER OF OIL AND GAS AND CHEMICAL EQUIPMENT MANUFACTURERS OF THE VORONEZH REGION." Vestnik Universiteta, no. 5 (July 16, 2020): 169–77. http://dx.doi.org/10.26425/1816-4277-2020-5-169-177.
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The form of integration of subjects as a cluster, in particular a cluster of producers of oil and gas and chemical equipment of the Voronezh region, which is a form of Association of interconnected companies in a certain territory, including scientific and technical and industrial enterprises, universities and research centers, has been considered. The experience of developed countries demonstrates the use of the cluster concept as an effective tool that contributes to improving the competitiveness of individual regions where clusters are based, as well as the country as a whole, and the development of individual economic sectors and participating enterprises. Within the framework of this study, the process of financial control of the Voronezh region cluster has been reviewed, and also implementation and efficiency of financing of the cluster of oil and gas and chemical equipment manufacturers have been analysed. As a result of the analysis, the strengths and weaknesses of the cluster have been revealed. Within the framework of research, the methods of bibliographic and logical analysis, systematic approach have been used.
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Ruban, Igor, Vitalii Martovytskyi, and Nataliia Lukova-Chuiko. "Designing a monitoring model for cluster super–computers." Eastern-European Journal of Enterprise Technologies 6, no. 2 (84) (December 14, 2016): 32–37. http://dx.doi.org/10.15587/1729-4061.2016.85433.
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Alfianto, Enggar, Siti Agustini, Syahri Muharom, Febdian Rusydi, and Ira Puspitasari. "Design Monitoring Electrical Power Consumtion at Computer Cluster." Journal of Physics: Conference Series 1445 (January 2020): 012027. http://dx.doi.org/10.1088/1742-6596/1445/1/012027.
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Cesca, Simone, Ali Tolga Şen, and Torsten Dahm. "Seismicity monitoring by cluster analysis of moment tensors." Geophysical Journal International 196, no. 3 (December 31, 2013): 1813–26. http://dx.doi.org/10.1093/gji/ggt492.
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Захаров, Игорь Евгеньевич, Олег Анатольевич Панарин, Сергей Георгиевич Рыкованов, Ришат Раилевич Загидуллин, Антон Константинович Малютин, Юрий Николаевич Шкандыбин, and Асель Ермековна Ермекова. "Monitoring applications on the ZHORES cluster at Skoltech." Program Systems: Theory and Applications 12, no. 2 (June 5, 2021): 73–103. http://dx.doi.org/10.25209/2079-3316-2021-12-2-73-103.
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Стандартные инструменты мониторинга для кластерных вычислительных систем позволяют оценить работу системы в целом, но не позволяют анализировать работу приложений по отдельности. Система мониторинга для измерения ресурсов, затребованных каждым приложением в отдельности разработана в Сколтехе для высокопроизводительного кластера ZHORES. Система мониторинга собирает как обычные метрики загрузки процессоров и графических ускорителей, так и счетчики событий ЦПУ/ГПУ, которые позволяют более детально анализировать тип ресурса, затребованный приложением. Сервисные программы, развернутые на каждом узле кластера, посылают результаты измерений в единую базу данных временных рядов с шагом в одну секунду. Эти данные затем анализируются статистическими методами в режиме оффлайн для выделения характеристик, связанных с использованием вычислительных ресурсов каждым приложением. Мониторинг позволяет выявлять неэффективное программное обеспечение, производить тонкую настройку работы кластера, а также улучшать работу высокопроизводительной системы в целом.
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TURNBULL, BRUCE W., ERIC J. IWANO, WILLIAM S. BURNETT, HOLLY L. HOWE, and LARRY C. CLARK. "MONITORING FOR CLUSTERS OF DISEASE: APPLICATION TO LEUKEMIA INCIDENCE IN UPSTATE NEW YORK." American Journal of Epidemiology 132, supp1 (July 1, 1990): 136–43. http://dx.doi.org/10.1093/oxfordjournals.aje.a115775.
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Abstract The authors propose a procedure for the detection of significant clusters of chronic diseases, with particular reference to cancer. The procedure allows for variations in population density and avoids the problem of “post hoc” formation of hypotheses or self-defined populations. This accounts for several of the principal problems of cluster evaluations. The techniques are practical but “computer-intensive.” The procedure, termed the “duster evaluation permutation procedure”, is applied to leukemia incidence data for an Upstate New York region obtained from the New York State Cancer Registry and census files. Comparisons are made with two other recently proposed clustering methods, namely the U-statistic method of WhKtemore et al. (Biometrika 1987;74:631–7) and the “geographical analysis machine” of Openshaw et al. (Lancet 1988; 1:272–3). Routine examination of disease occurrence with the cluster evaluation permutation procedure would allow state health officials to prioritize case investigations and to respond in a timely and efficient manner to inquiries of reported clusters.
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Brines, M., M. Dall'Osto, D. C. S. Beddows, R. M. Harrison, and X. Querol. "Simplifying aerosol size distributions modes simultaneously detected at four monitoring sites during SAPUSS." Atmospheric Chemistry and Physics Discussions 13, no. 10 (October 22, 2013): 27387–422. http://dx.doi.org/10.5194/acpd-13-27387-2013.
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Abstract. The analysis of aerosol size distributions is a useful tool for understanding the sources and the processes influencing particle number concentrations (N) in urban areas. Hence, during the one month SAPUSS campaign (Solving Aerosol Problems by Using Synergistic Strategies, EU Marie Curie Action) in autumn 2010 in Barcelona (Spain), four SMPS (Scanning Mobility Particle Sizers) were simultaneously deployed at four monitoring sites: a road side (RSsite), an urban background site located in the city (UBsite), an urban background located in the nearby hills of the city (Torre Collserola, TCsite) and a regional background site located about fifty km from the Barcelona urban areas (RBsite). The spatial distribution of sites allows study of the aerosol temporal variability as well as the spatial distribution, progressively moving away from urban aerosol sources. In order to interpret the datasets collected, a k-means cluster analysis was performed on the combined SMPS datasets. This resulted in nine clusters describing all aerosol size distributions from the four sites. In summary there were three main categories (with three clusters in each category): "Traffic" (Traffic 1 "Tclus1" – 8%, Traffic 2 "Tclus2" – 13%, Traffic 3, "Tclus3" – 9%), "Background Pollution" (Urban Background 1 "UBclus1" – 21%, Regional Background 1, "RBclus1" – 15%, Regional Background 2, "RBclus2" – 18%) and "Special cases" (Nucleation "NUclus" – 5%, Regional Nitrate, "NITclus" – 6%, and Mix "MIXclus" – 5%). As expected, the frequency of traffic clusters (Tclus1–3) followed the order RSsite, UBsite, TCsite, and RBsite. These showed typical traffic modes mainly distributed at 20–40 nm. The urban background sites (UBsite and TCsite) reflected also as expected urban background number concentrations (average values, N = 2.4×104 cm−3 relative to 1.2×105 cm−3 seen at RSsite). The cluster describing the urban background pollution (UBclus1) could be used to monitor the sea breeze circulation towards the regional background study area. Overall, the RBsite was mainly characterised by two different regional background aerosol size distributions: whilst both exhibited low N (2.6×103 for RBclus1 and 2.3×103 cm−3 for RBclus2), RBclus1 had average PM10 concentrations higher than RBclus1 (30 vs. 23 μg m−3). As regards the minor aerosol size distribution clusters, the "Nucleation" cluster was observed during daytime whilst the "Regional Nitrate" was mainly seen at night. The ninth cluster ("Mix") was the least well defined and likely composed of a number of aerosol sources. When correlating averaged values of N, NO2 and PM (particulate mass) for each k-means cluster, a linear correlation between N and NO2 with values progressively increasing from the regional site RBsite to the road site RSsite was found. This points to vehicular traffic as the main source of both N and NO2. By contrast, such an association does not exist for the case of the nucleation cluster, where the highest N is found with low NO2 and PM. Finally, the clustering technique allowed study of the impact of meteorological parameters on the traffic N emissions. This study confirms the shrinking of freshly emitted particles (by about 20% within 1 km in less than 10 min; Dall'Osto et al., 2011a) as particles are transported from the traffic hot spots towards urban background environments. Additionally, for a given well defined aerosol size distribution (Tclus2) associated to primary aerosol emissions from road traffic we found that N5–15 nm concentrations can vary up to a factor of eight. Within our measurement range (5–228 nm), we found that ultrafine particles within the range 5–15 nm are the most dynamic, being a complex ensemble of primary evaporating traffic particles, traffic tailpipe new particle formation and non-traffic new particle formation.
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DURSUN, HIKMET, KEVIN J. BARKER, DARREN J. KERBYSON, SCOTT PAKIN, RICHARD SEYMOUR, RAJIV K. KALIA, AIICHIRO NAKANO, and PRIYA VASHISHTA. "AN MPI PERFORMANCE MONITORING INTERFACE FOR CELL BASED COMPUTE NODES." Parallel Processing Letters 19, no. 04 (December 2009): 535–52. http://dx.doi.org/10.1142/s0129626409000407.
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In this paper, we present a methodology for profiling parallel applications executing on the family of architectures commonly referred as the "Cell" processor. Specifically, we examine Cell-centric MPI programs on hybrid clusters containing multiple Opteron and IBM PowerXCell 8i processors per node such as those used in the petascale Roadrunner system. We analyze the performance of our approach on a PlayStation3 console based on Cell Broadband Engine—the CBE—as well as an IBM BladeCenter QS22 based on PowerXCell 8i. Our implementation incurs less than 0.5% overhead and 0.3 µs per profiler call for a typical molecular dynamics code on the Cell BE while efficiently utilizing the limited local store of the Cell's SPE cores. Our worst-case overhead analysis on the PowerXCell 8i costs 3.2 µs per profiler call while using only two 5 KiB buffers. We demonstrate the use of our profiler on a cluster of hybrid nodes running a suite of scientific applications. Our analyses of inter-SPE communication (across the entire cluster) and function call patterns provide valuable information that can be used to optimize application performance.
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ZHANG, FENG, and ZHUJUN WENG. "MIXTURE PRINCIPAL COMPONENT ANALYSIS MODEL FOR MULTIVARIATE PROCESSES MONITORING." Journal of Advanced Manufacturing Systems 04, no. 02 (December 2005): 151–66. http://dx.doi.org/10.1142/s0219686705000631.
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A mixture probabilistic principal component analysis model is proposed as a process monitoring tool in this paper. High-dimensional measurement data could be aggregated into some clusters based on the mixture distribution model, where the number of these clusters are automatically determined from the maximum likelihood estimation procedures. It was illustrated that the mixture PCA models conform to the multivariate data well in the experiments involving Gaussian mixtures. The multivariate statistical process monitoring mechanism is then developed first with the learning of a finite mixture model with variant principal component within each cluster, followed by the construction of the statistical process confidence intervals for the identified regions or nodes from T2 charts. For the abnormal input measurement, they would fall out of the acceptance region set by the confidence control limits.
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Jankowska, Barbara, and Cezary Główka. "Clusters on the road to internationalization – evidence from a CEE economy." Competitiveness Review 26, no. 4 (July 18, 2016): 395–414. http://dx.doi.org/10.1108/cr-02-2015-0010.
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Purpose This paper aims to respond to the call for empirical research on cluster internationalization to learn more about the mechanism of cluster internationalization and the intensity of this process in a Polish context, which is specific due to Poland. Design/methodology/approach The authors provide a conceptual model of cluster internationalization and show results of their own primary, empirical and quantitative research on cluster internationalization in Poland. Findings The study reveals that Polish clusters are at the beginning of the internationalization process, particularly of the outward internationalization. The most popular mode of internationalization is exports. The level of Polish cluster internationalization operations is relatively low. Research limitations/implications Data were provided by cluster managers, but none of the cluster organizations had a reliable monitoring system for the international involvement of cluster members. These results are merely a snapshot; therefore, a longitudinal study should also be carried out. Social implications The research demonstrates that cluster companies can take advantage of internationalization operations performed by cluster organizations. The attempts of Polish government should be directed to push the internationalization of micros and small and medium-sized enterprises via cluster initiatives on the one hand and on the other hand to develop some kind of incentives for large companies to participate in cluster organizations as in reality exports are dominated by large firms. Originality/value The main value added of this paper consists in developing the conceptual model of cluster internationalization and applying the model in empirical research on the internationalization of Polish clusters. The study bridges the theory of firm internationalization, especially the approaches to firm internationalization with the theory of clusters. It offers insight into internationalization of clusters in Poland.
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Bhawsar, Pragya, and Utpal Chattopadhyay. "Evaluation of industry cluster competitiveness: a quantitative approach." Benchmarking: An International Journal 25, no. 7 (October 1, 2018): 2318–43. http://dx.doi.org/10.1108/bij-02-2017-0022.
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Purpose The purpose of this paper is to provide a quantitative approach to measure industry clusters competitiveness. Design/methodology/approach An attempt has been made to construct a composite indicator backed up by a conceptually grounded framework, by means of Analytical Hierarchical Process technique. Four industry clusters from auto sector in India are chosen for manifestation of the methodology. Findings The proposed methodology sufficiently emphasises on the order of significance of the factors/indicators that make a cluster competitive. The study demonstrates the comparative competitiveness performance of four select industry clusters from India. Research limitations/implications The methodology only focusses on auto clusters from India, application of the model/methodology needs to be extended to other set of industries that follows tier structure, or belong to other developing nations to corroborate the findings. Practical implications The proposed approach is a useful tool to provide guidance to policy-makers and in monitoring industry clusters progress. Originality/value The paper offers an empirical approach for measuring competitiveness of industry clusters. So far there has been only a minuscule research on cluster competitiveness using empirical methods specifically in case of developing countries like India. Because of the heterogeneity of actors in industry clusters and absence of cluster relevant databases, its performance has been mostly captured via means of case studies. This study is one of its kind that renders comparison of competitiveness across industry clusters by combining secondary data with the perception of cluster actors.
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Yevick, David, and Yong Hwan Lee. "A cluster controller for transition matrix calculations." International Journal of Modern Physics C 30, no. 04 (April 2019): 1950019. http://dx.doi.org/10.1142/s0129183119500190.
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We demonstrate that a temperature schedule for single-spin flip transition matrix calculations can be simply and rapidly generated by monitoring the average size of the Wolff clusters at a set of discrete temperatures. Optimizing this schedule yields a potentially interesting quantity related to the fractal structure of Ising clusters. We also introduce a technique in which the transition matrix is constructed at a sequence of discrete temperatures at which Wolff cluster reversals are alternated with certain series of single-spin flip steps. The single spin-flip transitions are then employed to construct a single transition matrix.
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Haikal, Fansuri Fikri, Rahmat Safe’i, and Arief Darmawan. "IMPORTANCE OF MONITORING OF FOREST HEALTH IN MANAGEMENT OF COMMUNITY FORESTS (Case Study of HKM Beringin Jaya managed by KTH Lestari Jaya 8)." JURNAL HUTAN PULAU-PULAU KECIL 4, no. 1 (April 1, 2020): 31–43. http://dx.doi.org/10.30598/jhppk.2020.4.1.31.
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Community forestry is a social forestry scheme in state forests. Hkm empowers communities around the forest area to increase the ability and independence of the local community. Forest health monitoring is still rarely applied in the management of HKm. Forest health monitoring results can be a reference in making the right decisions in managing HKm so that the results obtained can be optimal. This study aims to determine the results of forest health monitoring in Beringin Jaya HKm managed by KTH Lestari Jaya 8. The research was conducted using the Forest Health Monitoring (FHM) method. Forest health monitoring results show that there are 6 cluster plots with the final value of forest health status in cluster 1 (2.53) bad category, plot 2 (8.98) good category, plot 3 (6.31) moderate category, plot cluster 4 (10.51) category is good, cluster plot 5 (10.74) category is good and cluster plot 6 (8.98) category is good. Thus the results of forest health monitoring obtained by KTH Lestari Jaya 8 with an average final value of forest health status is moderate
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Zhang, Shaoyi, and Yang Liu. "Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data." Structural Health Monitoring 19, no. 6 (January 13, 2020): 1764–89. http://dx.doi.org/10.1177/1475921719895955.
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With the structural health monitoring technique, several medium- and small-span bridges possessing the same or similar structural characteristics located in a local road network are simultaneously monitored within one cluster. Nevertheless, insufficient research has been performed on detecting the damage of all the bridges monitored within one cluster under time-varying environmental temperatures. To address this issue, a method is proposed to assess the damage in all the bridges within one cluster utilizing the residual between the cumulative distribution functions of strain monitoring data. First, an algorithm for reconstructing the strain monitoring dataset is introduced, effectively improving the computational efficiency at removing as much measured noise from a large amount of monitoring data as possible. Second, a cluster analysis algorithm considering the similarity among the strain monitoring data at different measured points is proposed to classify the strain monitoring data for all the bridges monitored within one cluster. Different classes of strain monitoring data are established by identifying similar probability distribution patterns. Third, for each class, a damage detection index is proposed based on the residual between the cumulative distribution functions of strain monitoring data. All the bridges monitored within one cluster are acted upon by similar environmental temperatures during the same monitoring period; hence, the effects of the environmental temperature on the proposed index are mitigated indirectly during the same monitoring period. Thus, the proposed index is implemented to effectively detect the damage in all the bridges belonging to each class. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation and with strain monitoring data obtained from actual bridges.
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Alexandrova, Anna, and Yury Vladimirov. "Tourism clusters in Russia: What are their key features? The case of Vologda region." Worldwide Hospitality and Tourism Themes 8, no. 3 (June 13, 2016): 346–58. http://dx.doi.org/10.1108/whatt-02-2016-0007.
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Purpose This paper aims to review tourism cluster initiatives in Russia, particularly, tourism cluster formation processes, and investigate the role of local and federal government in promoting domestic tourism destinations with the help of tourism clusters. Design/methodology/approach This paper is formulated using a case study approach based on the lessons learned and the common practices in the Vologda region of the Russian Federation. Some results and key findings from field research were also used in the paper, as well as analysis of local and federal government activities, in attempting to promote domestic tourism in Russia. Findings Based on the analyses of the case study and review of common practices of tourism cluster creation and management, the paper draws on a review of the Vologda tourism cluster and profiles its main characteristic features: The context of “cluster” in Russian economy; Vologda tourism cluster was created by state initiative; Vologda tourism cluster indeed has its unique set of local (natural) competitive advantages; The rights of private companies (cluster members) are only consultative; Considerable funds are allocated by the Russian Federal Government and local government to cluster development; Vologda cluster is in an initial stage of formation, by the number and composition of members, Vologda cluster could be defined as a small cluster; There are no established cluster-specific quality of service standards; There is no defined cluster core in Vologda cluster; Cluster efficiency is measured by a set of metrics (gain in number of tourists, hotel rooms, etc.); Among the factors limiting Vologda cluster development, disunity on the federal and local government levels is substantial. Research limitations/implications This paper is currently limited in scope to the Vologda region case study and some results and key findings from field research selected by the researchers. The conditions for the selected case studies may not be identical in other Russian tourism clusters and locations (such as the Altay region cluster), and thus, the findings cannot be applied to other Russian tourism clusters without comparison studies. Originality/value The main output of this paper is a review of Russian tourism cluster formation and development based on the example of the Vologda region. It is of value to practitioners, researchers, policymakers and other stakeholders specializing in cluster research and tourism cluster development.
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Min, Jun-Ki. "CMOS: Efficient Clustered Data Monitoring in Sensor Networks." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/704957.
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Tiny and smart sensors enable applications that access a network of hundreds or thousands of sensors. Thus, recently, many researchers have paid attention to wireless sensor networks (WSNs). The limitation of energy is critical since most sensors are battery-powered and it is very difficult to replace batteries in cases that sensor networks are utilized outdoors. Data transmission between sensor nodes needs more energy than computation in a sensor node. In order to reduce the energy consumption of sensors, we present an approximate data gathering technique, called CMOS, based on the Kalman filter. The goal of CMOS is to efficiently obtain the sensor readings within a certain error bound. In our approach, spatially close sensors are grouped as a cluster. Since a cluster header generates approximate readings of member nodes, a user query can be answered efficiently using the cluster headers. In addition, we suggest an energy efficient clustering method to distribute the energy consumption of cluster headers. Our simulation results with synthetic data demonstrate the efficiency and accuracy of our proposed technique.
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Song, Xiaoying, Wei Sun, and Qilong Zhang. "A Dynamic Hierarchical Clustering Data Gathering Algorithm Based on Multiple Criteria Decision Making for 3D Underwater Sensor Networks." Complexity 2020 (October 21, 2020): 1–14. http://dx.doi.org/10.1155/2020/8835103.
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Data gathering is the basis of monitoring applications in an underwater sensor network, and excellent network coverage and data transmission reliability are the guarantees for the quality of monitoring tasks. However, the energy consumption of the nodes is too fast due to the heavy load of the cluster heads closer to the sink when data is transmitted between cluster heads (CHs) and the sink by multihop, which leads to an energy hole problem in an underwater sensor network of clustering technology. Aiming to address this problem, we propose a dynamic hierarchical clustering data gathering algorithm based on multiple criteria decision making (DHCDGA) in a 3D underwater sensor network. Firstly, the entire monitoring network is divided into many layers. For selecting a cluster head in each layer, multiple criteria decision making of an intuitionistic fuzzy Analytic Hierarchy Process (AHP) and hierarchical fuzzy integration is adopted. Furthermore, a sorting algorithm is used to form a clustering topology algorithm to solve the problem that there is the only node in one cluster. Then, an energy-balanced routing algorithm between clusters is proposed according to the residual energy of the node, the depth, and the number of neighbor nodes. Finally, the simulation results show that DHCDGA can not only effectively balance the energy consumption of the network and prolong the network lifetime but also improve network coverage and data gathering reliability.
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Zhong, Yuanchang, Lin Cheng, Liang Zhang, Yongduan Song, and Hamid Reza Karimi. "Energy-Efficient Routing Control Algorithm in Large-Scale WSN for Water Environment Monitoring with Application to Three Gorges Reservoir Area." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/802915.
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The typical application backgrounds of large-scale WSN (wireless sensor networks) for the water environment monitoring in the Three Gorges Reservoir are large coverage area and wide distribution. To maximally prolong lifetime of large-scale WSN, a new energy-saving routing algorithm has been proposed, using the method of maximum energy-welfare optimization clustering. Firstly, temporary clusters are formed based on two main parameters, the remaining energy of nodes and the distance between a node and the base station. Secondly, the algorithm adjusts cluster heads and optimizes the clustering according to the maximum energy-welfare of the cluster by the cluster head shifting mechanism. Finally, in order to save node energy efficiently, cluster heads transmit data to the base station in single-hop and multihop way. Theoretical analysis and simulation results show that the proposed algorithm is feasible and advanced. It can efficiently save the node energy, balance the energy dissipation of all nodes, and prolong the network lifetime.
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Kalaivani, S. "Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks." International Journal of Students' Research in Technology & Management 4, no. 2 (July 19, 2016): 38–43. http://dx.doi.org/10.18510/ijsrtm.2016.424.
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The wireless sensor networks consist of static sensors, which can be deployed in a wide environment for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of the sensor node is high. This results in reduced lifetime of the network. Some of the WSN architectures have been proposed based on Mobile Elements such as three-layer framework is for mobile data collection, which includes the sensor layer, cluster head layer, and mobile collector layer (called SenCar layer). This framework employs distributed load balanced clustering and dual data uploading, it is referred to as LBC-DDU.In the sensor layer a distributed load balanced clustering algorithm is used for sensors to self-organize themselves into clusters. The cluster head layer use inter-cluster transmission range it is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving in the inter-cluster communications. Through this transmissions cluster head information is send to the SenCar for its moving trajectory planning.This is done by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. Then the results show each cluster has at most two cluster heads. LBC-DDU achieves higher energy saving per node and energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sinks.
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Maulana, Irlan Rahmat, Rahmat Safe'i, and Indra Gumay Febryano. "Penilaian Status Kesehatan Hutan Mangrove Di Desa Margasari Kecamatan Labuhan Maringgai Kabupaten Lampung Timur." ULIN: Jurnal Hutan Tropis 5, no. 2 (September 30, 2021): 90. http://dx.doi.org/10.32522/ujht.v5i2.4765.
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Mangrove forest ecosystems can be interpreted as a unique and distinctive form of ecosystem, so that it is able to provide many benefits, ranging from socio-economic or ecological terms to the surrounding ecosystem. Mangrove forest in Margasari Village is a mangrove forest ecosystem that has physical, economic and ecological potential that needs to be maintained through sustainable forest management. One of the ways to manage mangroves is by monitoring forest health. Forest health monitoring that is applied periodically within a forest type can achieve sustainable forest management achievements so as to support better forest quality and quantity and can be a reference in making the right decisions in mangrove forest management so that the results obtained can be optimal. This study aims to obtain the value of the health status of mangrove forests in East Lampung Regency in order to ensure the sustainability of the forest. The study was conducted using themethod Forest Health Monitoring (FHM). The results of forest health monitoring showed that there were 4 plot clusters with the final value of forest health status in the medium category plot 1 (5.63), cluster plot 2 (3.51) poor category, cluster plot 3 (4.92) poor category, and cluster plot 4 (7.57) in good category. Thus the results of forest health monitoring obtained in the mangrove forest of Margasari Village with an average final value of forest health status of 5.41 which is included in the medium category.
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Hoppmann, Anna Lynn, Torrey Hill, Yanjun Chen, Wendy Landier, Lindsey Hageman, William E. Evans, Anne L. Angiolillo, et al. "Measuring mercaptopurine (6MP) adherence using red cell 6MP metabolite levels in children with acute lymphoblastic leukemia (ALL): A COG AALL03N1 study." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 10514. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.10514.
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10514 Background: Non-adherence to 6MP (monitored with medication event monitoring system [MEMs]) is associated with an increased risk of relapse in children with ALL.(JAMA Oncol, 2015) Self-report over-estimates true medication intake, particularly in non-adherent patients.(Blood, 2017) However, monitoring adherence using MEMs is logistically difficult. We investigated whether red cell 6MP metabolite levels (thioguanine nucleotide [TGN] and methylated mercaptopurine [MMP]) taken together, could identify non-adherent patients. Methods: The analysis included children with ALL in maintenance. To minimize variability in TGN and MMP levels due to pharmacogenetics, we excluded TPMT heterozygotes and homozygote mutants. We also excluded Asians to remove variability due to NUDT15. TGN and MMP levels were drawn at 6 consecutive monthly time points for each patient and averaged. TGN and MMP levels (pmol/8 x 108red cells) were standardized, adjusted for 6MP dose intensity, and then analyzed using cluster analysis (Spath, H. [1980]). Results: The 373 patients eligible for analysis yielded 5 clusters. Cluster #1 (n = 119; mean MMP: 15,656; mean TGN: 158); Cluster #2 (n = 211; MMP: 6,042; TGN: 135); and 3 very small outlying clusters (total N = 43). Adjusting for age, sex, race/ethnicity, cytogenetics and NCI risk, we found that patients in Cluster #2 were 2.6 times as likely to be non-adherent (MEMs-based adherence < 95%) compared to Cluster #1 (95% CI 1.5-4.4; P= 0.0007). Mean MEMs-based adherence was significantly higher for patients in Cluster #1 (94.3%) when compared to those in Cluster #2 (87.8%, p = 0.0002). Using Fine-Gray proportional subdistribution hazards models for competing risks and adjusting for clinical and sociodemographic factors, we found that patients in Cluster #2 were at a 2.3-fold higher risk of relapse compared with those in Cluster #1 (95%CI, 1.0-6.4, p = 0.058). Conclusions: These findings illustrate the potential for using a combination of red cell TGN and MMP levels in identifying non-adherent patients. We propose to use these and clinical and demographic factors associated with non-adherence in creating an adherence calculator.
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Brines, M., M. Dall'Osto, D. C. S. Beddows, R. M. Harrison, and X. Querol. "Simplifying aerosol size distributions modes simultaneously detected at four monitoring sites during SAPUSS." Atmospheric Chemistry and Physics 14, no. 6 (March 25, 2014): 2973–86. http://dx.doi.org/10.5194/acp-14-2973-2014.
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Abstract. The analysis of aerosol size distributions is a useful tool for understanding the sources and the processes influencing particle number concentrations (N) in urban areas. Hence, during the one-month SAPUSS campaign (Solving Aerosol Problems by Using Synergistic Strategies, EU Marie Curie Action) in autumn 2010 in Barcelona (Spain), four SMPSs (Scanning Mobility Particle Sizer) were simultaneously deployed at four monitoring sites: a road side (RSsite), an urban background site located in the city (UBsite), an urban background site located in the nearby hills of the city (Torre Collserola, TCsite) and a regional background site located about 50 km from the Barcelona urban areas (RBsite). The spatial distribution of sites allows study of the aerosol temporal variability as well as the spatial distribution, progressively moving away from urban aerosol sources. In order to interpret the data sets collected, a k-means cluster analysis was performed on the combined SMPS data sets. This resulted in nine clusters describing all aerosol size distributions from the four sites. In summary there were three main categories (with three clusters in each category): "Traffic" (Traffic 1, "Tclus_1" – 8%; Traffic 2, "Tclus_2" – 13%; and Traffic 3, "Tclus_3" – 9%) "Background Pollution" (Urban Background 1, "UBclus_1" – 21%; Regional Background 1, "RBclus_1" – 15%; and Regional Background 2, "RBclus_2" – 18%) and "Special Cases" (Nucleation, "NUclus" – 5%; Regional Nitrate, "NITclus" – 6%; and Mix, "MIXclus" – 5%). As expected, the frequency of traffic clusters (Tclus_1–3) followed the order RSsite, UBsite, TCsite, and RBsite. These showed typical traffic modes mainly distributed at 20–40 nm. The urban background sites (UBsite and TCsite) reflected also as expected urban background number concentrations (average values, N = 1.0 × 104 cm−3 and N = 5.5 × 103 cm−3, respectively, relative to 1.3 × 104 cm−3 seen at RSsite). The cluster describing the urban background pollution (UBclus_1) could be used to monitor the sea breeze circulation towards the regional background study area. Overall, the RBsite was mainly characterised by two different regional background aerosol size distributions: whilst both exhibited low N (2.7 × 103 for RBclus_1 and 2.2 × 103 cm−3 for RBclus_2), RBclus_1 had average PM10 concentrations higher than RBclus_2 (27 vs. 23 μg m−3). As regards the minor aerosol size distribution clusters, the "Nucleation" cluster was observed during daytime, whilst the "Regional Nitrate" was mainly seen at night. The ninth cluster ("Mix") was the least well defined and likely composed of a number of aerosol sources. When correlating averaged values of N, NO2 and PM (particulate mass) for each k-means cluster, a linear correlation between N and NO2 with values progressively increasing from the regional site RBsite to the road site RSsite was found. This points to vehicular traffic as the main source of both N and NO2. By contrast, such an association does not exist for the case of the nucleation cluster, where the highest N is found with low NO2 and PM. Finally, the clustering technique allowed study of the impact of meteorological parameters on the traffic N emissions. This study confirms the shrinking of freshly emitted particles (by about 20% within 1 km in less than 10 min; Dall'Osto et al., 2011a) as particles are transported from the traffic hot spots towards urban background environments. Additionally, for a given well-defined aerosol size distribution (Tclus_2) associated with primary aerosol emissions from road traffic we found that N5–15 nm concentrations can vary up to a factor of eight. Within our measurement range of SMPSs (N15–228 nm) and Condensation Particle Counters (CPCs, N>5 nm), we found that ultrafine particles within the range 5–15 nm in urban areas are the most dynamic, being a complex ensemble of primary evaporating traffic particles, traffic tailpipe new particle formation and non-traffic new particle formation.
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S., Manishankar, and S. Sathayanarayana. "Performance evaluation and resource optimization of cloud based parallel Hadoop clusters with an intelligent scheduler." International Journal of Engineering & Technology 7, no. 4.20 (November 29, 2018): 4220. http://dx.doi.org/10.14419/ijet.v7i4.13372.
Full textAbstract:
Data generated from real time information systems are always incremental in nature. Processing of such a huge incremental data in large scale requires a parallel processing system like Hadoop based cluster. Major challenge that arises in all cluster-based system is how efficiently the resources of the system can be used. The research carried out proposes a model architecture for Hadoop cluster with additional components integrated such as super node who manages the clusters computations and a mediation manager who does the performance monitoring and evaluation. Super node in the system is equipped with intelligent or adaptive scheduler that does the scheduling of the job with optimal resources. The scheduler is termed intelligent as it automatically decides which resource to be taken for which computation, with the help of a cross mapping of resource and job with a genetic algorithm which finds the best matching resource. The mediation node deploys ganglia a standard monitoring tool for Hadoop cluster to collect and record the performance parameters of the Hadoop cluster. The system over all does the scheduling of different jobs with optimal usage of resources thus achieving better efficiency compared to the native capacity scheduler in Hadoop. The system is deployed on top of OpenNebula Cloud environment for scalability.
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Kochetova, Zh Yu, O. V. Bazarsky, T. A. Kuchmenko, and N. V. Maslova. "Ecological Problems of the Aviation-Missile Cluster and Optimization of Geomonitoring Using a Piezo Sensor." Ecology and Industry of Russia 22, no. 8 (August 1, 2018): 32–38. http://dx.doi.org/10.18412/1816-0395-2018-8-32-38.
Full textAbstract:
The data of eleven-year monitoring of soil and subsoil contamination by kerosene, heavy metals, formaldehyde and nitrates on the territory of the aviation-missile cluster are presented. The adjusted index of total contamination with priority contaminants of soils and subsoil of various functional zones of the cluster (aviation-missile, industrial, residential-transport, recreational) have been calculated. Using the rank correlation coefficient of Spearman, a relationship was established between the content of priority contaminants in soil and subsoil. On the basis of the correlation analysis, the choice of kerosene as a marker compound for soil and subsoil contamination of air-missile clusters is justified. A piezo sensor was developed for the rapid analysis of kerosene in soil and subsoils in "on-site" mode without sampling and sample preparation. Optimization of ecological monitoring of soils and subsoil with the application of a test method for determining kerosene in the territories of aviation-missile clusters is proposed.
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Zhu, Kai, Yin Cheng Liang, and Xu Qiao. "Energy Saving Algorithm Research Based on Interior Monitoring System." Applied Mechanics and Materials 607 (July 2014): 681–84. http://dx.doi.org/10.4028/www.scientific.net/amm.607.681.
Full textAbstract:
Energy consumption is a main area in wireless sensor network researching. Interior monitoring system is designed by organized ZigBee wireless network. Address assignment mechanism is put forward and the result about sensor nodes is displayed. Aiming at the problems of LEACH algorithm that all nodes will be assigned cluster head, this paper proposes the algorithm that cluster head projected in first every round is effective until the next round so that reducing energy consumption in competition about cluster head. Through the simulation test, network address assignment is normal, data can be displayed through LED, and network telecommunication is stable.