Application of Clustering-based Decision Tree in the Screening of Maize Germplasm

[Objective] This paper aims to construct an improved fuzzy decision tree which is based on clustering,and researches into its application in the screening of maize germplasm.[Method] A new decision tree algorithm based upon clustering is adopted in this paper,which is improved against the defect that traditional decision tree algorithm fails to handle samples of no classes.Meanwhile,the improved algorithm is also applied to the screening of maize varieties.Through the indices as leaf area,plant height,dry weight,potassium（K） utilization and others,maize seeds with strong tolerance of hypokalemic are filtered out.[Result] The algorithm in the screening of maize germplasm has great applicability and good performance.[Conclusion] In the future more efforts should be made to compare improved the performance of fuzzy decision tree based upon clustering with the performance of traditional fuzzy one,and it should be applied into more realistic problems.

Fast density peak-based clustering algorithm for multiple extended target tracking

The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.

Cooperative Subcarrier Sensing Using Antenna Diversity Based Weighted Virtual Sub Clustering

The idea of cooperation and the clustering amongst cognitive radios(CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing(SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing(CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing(OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM(NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining(AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed.

Clustering algorithm based on density function and nichePSO

This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and in- creases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clus- tering problem, which can be useful for the niches＇ forming and searching. At last, a novel method that distributes samples to the corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters auto- matically and accurately.

A coin-tap method of composite materials non-destructive testing based on improved grey clustering

Aiming at the problems of low reliability and complex operation of traditional coin-tap test of composite material,this paper introduces the grey system theory and achieves better performance.The response signals of coin-tap are classified through the grey clustering based on relation analysis,and corresponding improvements are made to the calculation method of the relation degree of nearness.First,the time history of acceleration is taken as the system behavior sequence.The improved correlation calculation method is used to solve the relation degree of nearness between the sequences,and the matrix of degree of grey relation is constructed based on this.Then,the sequence groups are summarized through the matrix,and the response signals of coin-tap are qualitatively classified according to the location of the reference sequence.Finally,the defect detection of composite materials is completed without pre-testing.The test results show that the accuracy of the coin-tap test based on improved grey clustering reaches 100%,which simplifies the operation steps while ensuring the reliability of the coin-tap test results.

Clustering Network Topology Control Method Based on Responsibility Transmission

The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission in wireless sensor network is concluded as a kind of responsibility transmission. By redefining the responsibility and availability of nodes, the strategy for cluster head selection is studied, the responsibility and availability is determined by the combination of the residual energy, location and current flow of nodes. Based on the above, new clustering network topology control algorithm based on responsibility transmission CNTCABRT and hierarchical multi-hop CNTCABRT is presented in this paper, whose algorithm structure is along the famous LEACH algorithm. Experimental result demonstrates its promising performance over the famous LEACH algorithm in the cluster head selection, the size of cluster, the deployment of nodes and the lifetime of nodes, and several innovative conclusions are proposed finally.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

A Content-Based Parallel Image Retrieval System on Cluster Architectures

We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. Key words content-based image retrieval - cluster architecture - color-spatial feature - B/S mode - task parallel - WWW - Internet CLC number TP391 Foundation item: Supported by the National Natural Science Foundation of China (60173058)Biography: ZHOU Bing (1975-), male, Ph. D candidate, reseach direction: data mining, content-based image retrieval.

Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network(WSN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications.It comprises a massive set of nodes with restricted energy and processing abilities.Energy dissipation is a major concern involved in the design of WSN.Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms.In order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for WSN.The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way.The presented HBAC-AVOR model follows a two stage process.At the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing afitness function involving many input parameters.Next,the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol.On comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Preparation, Structures and Magnetic Properties of Two Co-based Clusters with Triangular Core and Cuboidal Core Geometries

Two novel Co-based clusters with the 2-(hydroxylmethyl)pyridine(hmpH)ligand,formulated as[Co3(hmp)6(hmpH)]×2NO3×3H2O(ZTU-3)and[Co4(hmp)4(CH3CO2)2(H2O)4]×2NO3(ZTU-4),have been successfully synthesized and structurally characterized.ZTU-3 features a triangular core geometry,while ZTU-4 exhibits a cuboidal core geometry.In addition,the magnetic properties of ZTU-3 and ZTU-4 are also all investigated.

Metamodel-based Global Optimization Using Fuzzy Clustering for Design Space Reduction

High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.

A Silicon Cluster Based Single Electron Transistor with Potential Room-Temperature Switching

We demonstrate the fabrication of a single electron transistor device based on a single ultra-small silicon quantum dot connected to a gold break junction with a nanometer scale separation. The gold break junction is created through a controllable electromigration process and the individual silicon quantum dot in the junction is deter- mined to be a Si 170 cluster. Differential conductance as a function of the bias and gate voltage clearly shows the Coulomb diamond which confirms that the transport is dominated by a single silicon quantum dot. It is found that the charging energy can be as large as 300meV, which is a result of the large capacitance of a small silicon quantum dot （-1.8 nm）. This large Coulomb interaction can potentially enable a single electron transistor to work at room temperature. The level spacing of the excited state can be as large as 10meV, which enables us to manipulate individual spin via an external magnetic field. The resulting Zeeman splitting is measured and the g factor of 2.3 is obtained, suggesting relatively weak electron-electron interaction in the silicon quantum dot which is beneficial for spin coherence time.

Destination Based Stable Clustering Algorithm and Routing for VANET

Now in modern telecommunication, one of the big topic research is a Vehicle Ad-hoc Network “VANET” (V2V). This topic is one of an “issues of the day” because research has problematic topic due to its many application-questions, what we need to solve: avoid collisions, any accidents on a way, and notifications about congestions on the road, available car parking, road-side commercial-business ads, and etcetera. These like application forms creating big delay constraining’s i.e. the instant data should reach the destination within certain time limits. Therefore, we need a really efficient stable clustering method and routing in vehicle ad-hoc network which will be resistant to network delays and meets network requirements. The methods are proposed in the paper for optimization VANETs data traffic as well as to minimizing delay. First, here is presented, a stable clustering algorithm based on the destination, contextually take into consideration various physical parameters for cluster formation such as location of the vehicle and its direction, vehicle speed and destination, as well as a possible list of interests of the vehicle. And also the next main process is to depend on these “five parameters” we can calculate the “Cluster Head Eligibility” of each car. Second, based on this “Cluster Head Eligibility”, described cluster head selection method. Third, for efficient communication between clusters, present a routing protocol based on the “destination”, which considered an efficient selecting method of next forwarding nodes, which is calculated by using “FE” metric.

Improved Clustering Algorithm Based on Density-Isoline

An improved clustering algorithm was presented based on density-isoline clustering algorithm. The new algorithm can do a better job than density-isoline clustering when dealing with noise, not having to literately calculate the cluster centers for the samples batching into clusters instead of one by one. After repeated experiments, the results demonstrate that the improved density-isoline clustering algorithm is significantly more efficiency in clustering with noises and overcomes the drawbacks that traditional algorithm DILC deals with noise and that the efficiency of running time is improved greatly.

LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream

Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream), a density-based clustering algorithm using leader clustering. The algorithm is based on a two-phase clustering. The online phase selects the proper mini-micro or micro-cluster leaders based on the distribution of data points in the micro clusters. Then, the leader centers are sent to the offline phase to form final clusters. In LeaDen-Stream, by carefully choosing between two kinds of micro leaders, we decrease time complexity of the clustering while maintaining the cluster quality. A pruning strategy is also used to filter out real data from noise by introducing dense and sparse mini-micro and micro-cluster leaders. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.

Cluster Based Strategy to Improve Competitiveness of Rare Earth Industry in China——Taking Emerging Rare Earth Industry Cluster in Baotou As An Example

China′s rare earth industry has shown its strong competence, but notably this competence is mostly based on the comparative advantages in natural resources and production costs that will not provide a long-term momentum for the growth of the industrial competitiveness. It has become an urgent task now for China′s rare earth industry to improve its competitiveness through adjusting the product structure and enhancing the technological application, to gain much healthier and more sustainable growth in the future. This paper aims to present a new development strategy based on the industry cluster theory to improve the competitiveness of China′s rare earth industry with a case study on the emerging rare earth industry cluster in Baotou. The paper first reviews the cluster theory and explains why it matters to industrial competitiveness briefly, and then proposes a general framework for implementing cluster policies and strategies in practice. Following that, a case to study the emerging rare earth industry cluster in Baotou was presented and a cluster initiative based on the general framework was developed. The objectives of the cluster initiative and the role of government on initiative organizing and cluster upgrading were discussed in detail. In the end, general implications drawn from the case of Baotou were summarized, which try to provide some insights for applying cluster-based strategies to the rare earth industry of China.

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.

Mean Territorial Energy Based Clustering Protocol for Randomly Deployed Wireless Sensor Networks

Clustering algorithms can balance the power consumption of energy constraint wireless sensor networks. This paper proposes a new clustering protocol called Mean Territorial Energy Based Clustering Protocol (MTEP) for randomly deployed wireless sensor networks. In MTEP, cluster heads are selected according to residual energy and location information of a node in current round as well as mean territorial energy and total base station distance of node’s corresponding cluster territory in previous round. Energy consumption in conventional protocols becomes unbalanced because of clusters having different lengths. Proposed MTEP protocol addresses this problem by setting thresholds on cluster length and node to cluster head distance for producing equal length clusters. Simulation results show that MTEP protocol extends network lifetime and stability with reduction in energy dissipation compared to other clustering protocols such as LEACH and REAC.

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.