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.

Scheduling Dual-Arm Cluster Tools With Multiple Wafer Types and Residency Time Constraints

Accompanying the unceasing progress of integrated circuit manufacturing technology, the mainstream production mode of current semiconductor wafer fabrication is featured with multi-variety, small batch, and individual customization, which poses a huge challenge to the scheduling of cluster tools with single-wafer-type fabrication. Concurrent processing multiple wafer types in cluster tools, as a novel production pattern, has drawn increasing attention from industry to academia, whereas the corresponding research remains insufficient. This paper investigates the scheduling problems of dual-arm cluster tools with multiple wafer types and residency time constraints. To pursue an easy-to-implement cyclic operation under diverse flow patterns,we develop a novel robot activity strategy called multiplex swap sequence. In the light of the virtual module technology, the workloads that stem from bottleneck process steps and asymmetrical process configuration are balanced satisfactorily. Moreover, several sufficient and necessary conditions with closed-form expressions are obtained for checking the system's schedulability. Finally, efficient algorithms with polynomial complexity are developed to find the periodic scheduling, and its practicability and availability are demonstrated by the offered illustrative examples.

A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.

Clustering of Multiple Risk Behaviors among Ethnically Diverse Adolescents Living in Hawaii

The current study examined the prevalence and clustering of 5 health-risk behaviors among adolescents in Hawaii, including physical inactivity, low fruit and vegetable consumption, junk food consumption, excessive television time, and inadequate sleep. High school students were recruited from 5 classrooms in Oahu Hawaii. Data were collected in the spring semester of 2011. Proportions were used to describe the prevalence of single and multiple health risk behaviors. Significant health behavior clusters were revealed using an observed-to-expected (O/E) ratio method. Participating adolescents (n = 114) were 11th and 12th grade students with a mean age of 16.28 (SD = 0.62). Participants were predominantly female (75%) and Filipino-American (68%). Seventy-seven percent of adolescents were physically inactive, 90% watched excessive TV, 66% consumed inadequate fruits and vegetables, 94% reported inadequate levels of sleep, and 80% consumed excessive junk food. Overall, 94% reported at least 3 risk factors, 73% reported at least 4 risk factors, and 37% reported all 5 risk factors. No significant clusters were found. Conclusion: Health-risk behaviors cluster and occur more often than expected among adolescents living in Hawaii. Non-significant clustering may be due to insufficient variability within the sample data;future examinations of this highly understudied population are necessary.

Effects of EMS Treatments on Multiplication and Differentiation of Sugarcane Embryonic Cell Clusters

[ Objective] This study aimed to investigate the effects of EMS （ethyl methane sulfonate） treatments on multiplication and differentiation of sugarcane embryonic cell clusters. [ Method] Sugarcane variety Xintaitang 22 （ROC22） was used as the experimental material. After treated with different concentrations of EMS for different time, sugarcane embryonic cell clusters were collected for subculture, differentiation and rooting, to compare and analyze the correlation of differ- ent EMS treatments with the muhiplicafion and differentiation of sugarcane embryonic cell clusters. [ Result] Treating ROC22 embryonic cell clusters with 0. 10% -0.15% EMS for4-6 h led to the best results, which reached the level of semi-lethal dose. Sugarcane embryonic cell clusters treated with EMS were adopted for subculture; results indicated that browning rate of cell clusters was higher than that in control （CK） but embryonic structure proportion was lower than that in control ; in addition, multiplication multiple of sugarcane embryonic cell clusters was also lower than that in control. After treated with EMS, sugarcane em- bryonic cell clusters exhibited significantly lower bud differentiation rate and higher browning rate compared with control. Furthermore, treating sugarcane embryonic cell clusters with 0.15% EMS for 2 h was conducive to plantlet emergence and rooting of sugarcane embryonic cell clusters. [ Conclusion] This study provided the- oretical basis for effective mutagenesis of sugarcane using EMS.

Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment

Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.

A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs

As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.

Cruise missile multiple routes planning based on hybrid particle swarm optimization

In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization （ HPSO ） algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to divide the particle swarm into multiple isolated sub-populations, then niche algo- rithm is adopted to make all particles independently search for optimal values in their own sub-popu- lations. Finally simulated annealing （SA） algorithm is introduced to avoid the weakness of PSO algo- rithm, which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route, multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate, and the planned routes have flat ballisticpaths and short ranges which meet the low-altitude penetration requirements.

K-DSA for the multiple traveling salesman problem

Aimed at a multiple traveling salesman problem(MTSP)with multiple depots and closed paths,this paper proposes a k-means clustering donkey and a smuggler algorithm(KDSA).The algorithm first uses the k-means clustering method to divide all cities into several categories based on the center of various samples;the large-scale MTSP is divided into multiple separate traveling salesman problems(TSPs),and the TSP is solved through the DSA.The proposed algorithm adopts a solution strategy of clustering first and then carrying out,which can not only greatly reduce the search space of the algorithm but also make the search space more fully explored so that the optimal solution of the problem can be more quickly obtained.The experimental results from solving several test cases in the TSPLIB database show that compared with other related intelligent algorithms,the K-DSA has good solving performance and computational efficiency in MTSPs of different scales,especially with large-scale MTSP and when the convergence speed is faster;thus,the advantages of this algorithm are more obvious compared to other algorithms.

ADF Studies of Neutral Small C_n(n=3～6)High-symmetry Clusters

The geometries, electronic structures of pure carbon clusters were investigated in the range of C 3 to C 6 size. To obtain accurate results, the STO double zeta basis sets with a polarization function are selected. The main focuses are on the new geometry, electronic structures of carbon clusters, such as the three dimensional structure for C 4, C 5 and so on. The clusters are found to have more isomers corresponding to different geometries and spin multiplicities close to their ground states. We find that the spin multiplicity of the ground state or close to ground state of odd numbered linear chain is 1. But in even numbered linear chains, the spin multiplicity is 3. In odd numbered cyclic structure without branch chain, the spin multiplicity of the ground state or close ground state is 3, and that of even numbered is 1. But the three dimensional structure disagree those principles.

Blind localization of multiple primary users without number knowledge

A novel multiple PUs (Primary Users) localization algorithm was proposed, which estimates the number of PUs by SVD (Singular Value Decomposition) method and seeks non-cooperative PUs' position by executing k-mean clustering and iterative operations. The simulation results show that the proposed method can determined the number of PUs blindly and achieves better performance than traditional expectation-maximization (EM) algorithm.

MULTIPLE-SCATTERING STUDIES OF ADSORPTION STRUCTURE OF C_2D_2/Si(111)7×7

The multiple scattering cluster (MSC) method has been employed to perform a theoretical analysis on carbon is near edge X-ray absorption fine structure of the deuteron acetylene (C2 D2) adsorbed on Si(111)7× 7 at room temperature. From the MSC study. it is confirmed that the (22D2 molecule is bonded to a pair of adjacent Si adatom and Si restatom with C-Si bond length about 0.18nm. The carbon-deuteron bond is bent away front the surface and the CCD bond angle is about 120°. The molecule plane tilt slightly away from the surface normal. Compared with C2D2 in gas phase, the C-C bond and C-D bond are elongated by about 0.03nm and 0.02nm respectively when acetylene was adsorbed on the subtrate. Keyowrds: adsorption of deuteron acetylene on Si(111)7×7. near edge X- ray absorption fine structure. multiple scattering cluster method

Performance Improvement of Multi-User Multiple-Input Multiple-Output Protocol for WLAN

The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.

老年人群慢性病共病模式与失能状况的关联研究:基于四川省抽样调查

背景随着人口老龄化的进展,慢性病共病和失能老年人的数量与日俱增,给社会带来沉重的医疗负担。共病和失能联系紧密,但目前关于共病模式和失能状况的相关性研究较少。目的本研究旨在以四川省为例识别我国老年人群中常见的慢性病共病模式,并从个体层面探讨不同共病模式与失能状况之间的相关性。方法于2022年8—11月采用定额随机抽样方法在四川省抽取501例60岁及以上的老年人群样本,收集其慢性病患病状况、失能状况及一般人口学等信息。通过自组织映射神经网络和K-Means相结合的二次聚类方法,识别老年人群的常见共病模式。基于2021年国家医疗保障局首个《长期护理失能等级评估标准(试行)》判定样本的失能等级,应用逻辑回归模型探究慢性病共病模式与失能等级之间的关系。结果501例样本中,共病患病率为62.3%(312/501),失能率为74.3%(372/501);最终确定6种共病模式:关节炎或风湿病-高血压模式;血脂异常-高血压模式;肾脏疾病-关节炎或风湿病模式;癌症-关节炎或风湿病模式;哮喘-高血压-消化系统疾病模式;情感精神-记忆相关疾病模式。二分类逻辑回归分析结果显示,共病人群的失能风险是无共病人群的6.3倍(OR=6.3,95%CI=3.9~10.3,P<0.05)。多因素多分类逻辑回归分析结果显示,6种共病模式的失能风险均增加(P<0.05);其中情感精神-记忆相关疾病模式的共病人群失能风险最大,是无共病人群的10.7倍(OR=10.7,95%CI=1.7~63.6),其次是癌症-关节炎或风湿病模式(OR=7.8,95%CI=2.4~24.8)。结论四川省老年人共病患病率较高,多种共病模式均与失能的发生显著相关,尤其是情感精神-记忆相关疾病模式和癌症-关节炎或风湿病模式。医疗卫生保健系统应重点关注患有共病的老年群体,基于不同共病模式制定精准有效的长期护理政策和策略,预防延缓失能的发生、发展,提高老年人健康福祉,节约社会医疗卫生资源。

An Efficient Stacked-LSTM Based User Clustering for 5G NOMA Systems

Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.

RAPIDITY DISTRIBUTION IN DIFFERENT MULTIPLICITY INTERVALS

Based on the statistical model proposed by the Berliner group,including the assumption of cluster production and considering energy conservation for each fixed total multiplicity n,good fits to the data of the rapidity distributions for different mutiplicity intervals are obtained.

A Fuzzy Clustering Algorithm Based on Multipath Component Trajectory for Millimeter Wave Radio Channels

Time-varying channel modeling plays an important role for many applications in time-variant scenarios,while most clustering algorithms focus on static channels and cannot accurately model the channel time-evolution properties.In this paper,a fuzzy clustering algorithm based on multipath component(MPC)trajectory is proposed.Firstly,both the distance and velocity similarities of the MPCs in different snapshots are taken into account to track the MPC trajectory,in which the fuzzy scheme and a kernel function are used to calculate the distance and velocity similarities,respectively.Secondly,a fuzzy MPC trajectory clustering algorithm is proposed to cluster the MPCs in multiple snapshots.The MPCs in a snapshot are clustered according to the membership,which is defined as the probability that a MPC belongs to different clusters.Finally,time-varying channels at 28 GHz are simulated to validate the performance of our proposed algorithm.The results show that our proposed algorithm is able to accurately identify the clusters in time-varying channels compared with the existing clustering algorithms.

A New Integrated Fuzzifier Evaluation and Selection (NIFEs) Algorithm for Fuzzy Clustering

Fuzzy C-means (FCM) is simple and widely used for complex data pattern recognition and image analyses. However, selecting an appropriate fuzzifier (m) is crucial in identifying an optimal number of patterns and achieving higher clustering accuracy, which few studies have investigated. Built upon two existing methods on selecting fuzzifier, we developed an integrated fuzzifier evaluation and selection algorithm and tested it using real datasets. Our findings indicate that the consistent optimal number of clusters can be learnt from testing different fuzzifiers for each dataset and the fuzzifier with the lowest value for this consistency should be selected for clustering. Our evaluation also shows that the fuzzifier impacts the clustering accuracy. For longitudinal data with missing values, m = 2 could be an empirical rule to start fuzzy clustering, and the best clustering accuracy was achieved for tested data, especially using our multiple-imputation based fuzzy clustering.

Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model

This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications.The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chestX-ray images through Two Step-As clustering algorithm with rich filter families,abstraction and weight-sharing properties.In contrast to the generally used transformational learning approach,the proposed model was trained before and after clustering.The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group,with each subject group displayed as a distinct category.The retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method,which was then utilised to classify the instances.The Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray findings.Models forMultiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they include.The testswere carried out using theCOVID-19 public radiology database,and a cross-validationmethod ensured accuracy.The proposed classifier with an accuracy of 98.02%percent was found to provide the most efficient outcomes possible.The result is a lowcost,quick and reliable intelligence tool for detecting COVID-19 infection.