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Fuzzy Logic-Based Path Planning for Data Gathering Mobile Sinks in WSNs

IEEE ACCESS 2021年 9卷 96002-96020页

作者： Punriboon, Chatchai So-In, Chakchai Aimtongkham, Phet Leelathakul, Nutthanon Khon Kaen Univ Fac Sci Dept Comp Sci Appl Network Technol ANT Lab Khon Kaen 40002 Thailand Burapha Univ Fac Informat Chon Buri 20131 Thailand

Mobile sinks (MSs) are capable of collecting data along specified paths in wireless sensor networks (WSNs). They are deployed as a popular alternative for data loggers, to which all nodes have to send sensory data. If MSs' paths (or cycles) are not well determined, it might take a relatively long time for the MSs to make a round trip. Recent research works have proposed methods to determine rendezvous points (RPs) that the MSs must pass by to collect data, with the aim of reducing the data-collection time. Determination of the number of RPs is important, and it is challenging to make ensure that there are sufficient RPs widely located throughout a sensor network, forming a circle along which the MSs can spend limited time traveling. This research presents a method for designing paths and pinpointing RPs for MSs to collect data, as well as determining the next hop to relay data for each sensor node. Instead of reducing the MSs' travel time, the focus of this research is to preserve the energy of all sensor nodes in WSNs. Our method determines the maximum number of RPs such that the MSs can run through each RP's communication range (within a time constraint) without depleting their own energy. The method comprises three main steps. First, we calculate the number of RPs and design the MS path. Second, the exact data-collection points are determined. The last step is to specify the path along which sensory data are relayed to the MS. In our experiments, we simulate two WSNs of different sizes. The results show that our method outperforms the others by 70%-80% in terms of the sensor node uptime, power consumption, MS traveling time and the number of RPs.

关键词： Wireless sensor networks Fuzzy logic Planning Relays clustering algorithms Power demand Data collection Data gathering fuzzy logic system mobile sink path planning rendezvous point wireless sensor network

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Graph Based Training Resource Allocation Scheme for CoMP Transmission in CRAN: A Low Complexity Solution

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第3期8卷 2402-2411页

作者： Singh, Rohit Saluja, Deepak Kumar, Suman Indian Inst Technol Ropar Dept Elect Engn Ropar 140001 Punjab India

Coordinated Multi Point (CoMP) transmission in Cloud Radio Area Network (CRAN) strives to provide the required Quality of Service (QoS), irrespective of user's location. Specifically, CoMP enables intra-cluster Access Points (APs) to maximize user's performance utilizing channel information, which is obtained by exchanging prior-known pilot signals among the intended users and intra-cluster APs. These signals are often referred as training signals, and are shared over Training Resources (TRs). However, CoMP is pillared on user-centric network, thereby the resulting clusters are inevitably overlapped. As a result, the TR allocation schemes for these networks require considerably large computational complexity. In this paper, we introduce the notion of Time Slot Tagging (TST), in which each AP is tagged with an appropriate training slot. Further, clusters are formed on the basis of this tagged information. Specifically, TST eliminates the requirement of resource allocation for individual users which saves a significant time in each clustering cycle, and thus enhances the link utilization factor. Also, TST is designed in a manner that it assures the presence of preferred APs in each intended cluster. Further, it is shown that the proposed approach performs significantly close to the most optimistic scenario.

关键词： Resource management Training clustering algorithms Quality of service Downlink Data communication Tagging CRAN CoMP Graph Based Clusters

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Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第4期18卷 1524-1534页

作者： Sharma, Gaurav Rana, Prashant Singh Bawa, Seema Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala 147004 Punjab India

Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrovirus known as Human T-cell Lymphotropic virus (HTLV). Out of the four different types of HTLVs, HTLV-1 is most prominent in scourging over 20 million people around the world and still not much effort has been made in understanding the epidemiology and controlling the prevalence of this virus. This condition further worsens when most of the infected cases remain asymptomatic throughout their lifetime due to the limited diagnostic methods;that are most of the times unavailable for timely detection of infected individuals. Moreover, at present, there is no licensed vaccination for HTLV-1 infection. Therefore, there is a need to develop the faster and efficient diagnostic method for the detection of HTLV-1. Influenced from the outcomes of the machine learning techniques in the field of bio-informatics, this is the first study in which 64 hybrid machine learning techniques have been proposed for the prediction of different type of HTLVs (HTLV-1, HTLV-2, and HTLV-3). The hybrid techniques are built by permutation and combination of four classification methods, four feature weighting, and four feature selection techniques. The proposed hybrid models when evaluated on the basis of various model evaluation parameters are found to be capable of efficiently predicting the type of HTLVs. The best hybrid model has been identified by having accuracy, an AUROC value, and F1 score of 99.85 percent, 0.99, and 0.99, respectively. This kind of the system can assist the current diagnostic system for the detection of HTLV-1 as after the molecular diagnostics of HTLV by various screening tests like enzyme-linked immunoassay or particle agglutination assays there is always a need of confirmatory tests like western blotting, immuno-fluorescence

关键词： Proteins Viruses (medical) Feature extraction clustering algorithms Bioinformatics Machine learning Predictive models HTLV prediction machine learning random forest K-means feature selection heuristic technique

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Approaches for Multi-View Redescription Mining

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IEEE ACCESS 2021年 9卷 19356-19378页

作者： Mihelcic, Matej Smuc, Tomislav Dept Math Fac Sci Zagreb 10000 Croatia Rudjer Boskovic Inst Zagreb 10000 Croatia

The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain redescriptions and allow for the exploration and analyses of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework can use any multi-target regression or multi-label classification algorithm, with models that can be represented as sets of rules, to generate redescriptions. Multi-view redescriptions are built using incremental view-extending heuristic from initially created two-view redescriptions. In this work, we use different types of Predictive clustering trees algorithms (regular, extra, with random output selection) and the Random Forest thereof in order to improve the quality of final redescription sets and/or execution time needed to generate them. We provide multiple performance analyses of the proposed framework and compare it against the naive approach to multi-view redescription mining. We demonstrate the usefulness of the proposed multi-view extension on several datasets, including a use-case on understanding of machine learning models - a topic of growing importance in machine learning and artificial intelligence in general.

关键词： Data mining Task analysis Vegetation Indexes Prediction algorithms clustering algorithms Complexity theory Knowledge discovery multi-view redescription mining redescription set predictive clustering trees random forest extremely randomized trees random output selection

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Joint Optimization of Beamforming, Phase-Shifting and Power Allocation in a Multi-Cluster IRS-NOMA Network

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第8期70卷 7705-7717页

作者： Xie, Ximing Fang, Fang Ding, Zhiguo Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England Univ Durham Dept Engn Durham DH1 3LE England

The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme.

关键词： NOMA Array signal processing Optimization Downlink Resource management Minimization clustering algorithms Intelligent reflective surface (IRS) non-orthogonal multiple access (NOMA) transmit power optimization

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WMLRR: A Weighted Multi-View Low Rank Representation to Identify Cancer Subtypes From Multiple Types of Omics Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第6期18卷 2891-2897页

作者： Sun, Yesen Le Ou-Yang Dai, Dao-Qing Sun Yat Sen Univ Sch Math Dept Stat Guangzhou 510275 Guangdong Peoples R China Shenzhen Univ Coll Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China

The identification of cancer subtypes is of great importance for understanding the heterogeneity of tumors and providing patients with more accurate diagnoses and treatments. However, it is still a challenge to effectively integrate multiple omics data to establish cancer subtypes. In this paper, we propose an unsupervised integration method, named weighted multi-view low rank representation (WMLRR), to identify cancer subtypes from multiple types of omics data. Given a group of patients described by multiple omics data matrices, we first learn a unified affinity matrix which encodes the similarities among patients by exploring the sparsity-consistent low-rank representations from the joint decompositions of multiple omics data matrices. Unlike existing subtype identification methods that treat each omics data matrix equally, we assign a weight to each omics data matrix and learn these weights automatically through the optimization process. Finally, we apply spectral clustering on the learned affinity matrix to identify cancer subtypes. Experiment results show that the survival times between our identified cancer subtypes are significantly different, and our predicted survivals are more accurate than other state-of-the-art methods. In addition, some clinical analyses of the diseases also demonstrate the effectiveness of our method in identifying molecular subtypes with biological significance and clinical relevance.

关键词： Cancer Data models clustering algorithms Sparse matrices Genomics Data mining clustering methods Multi-view learning subtype identification low rank representation omics data integration

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Typical Characteristic-Based Type-2 Fuzzy C-Means Algorithm

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第5期29卷 1173-1187页

作者： Yang, Xiyang Yu, Fusheng Pedrycz, Witold Quanzhou Normal Univ Key Lab Intelligent Comp & Informat Proc Fujian P Quanzhou 362000 Peoples R China Beijing Normal Univ Sch Math Sci Beijing 100875 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Type-2 fuzzy sets provide an efficient vehicle for handling uncertainties of real-world problems, including noisy observations. Bringing type-2 fuzzy sets to clustering algorithms offers more flexibility to handle uncertainties associated with membership concepts caused by a noisy environment. However, the existing type-2 fuzzy clustering algorithms suffer from a time-consuming type-reduction process, which not only hampers the clustering performance but also increases the burden of understanding the clustering results. In order to alleviate the problem, this article introduces a set of typical characteristics of type-2 fuzzy sets and establishes a characteristic-based type-2 fuzzy clustering algorithm. Being different from the objective function used in the fuzzy C-means (FCM) algorithm that produces cluster centers and type-1 memberships, the objective function in the proposed algorithm contains additional characteristics of type-2 membership grades, namely, centers of gravity and cardinalities of the secondary fuzzy sets. The derived iterative formulas used for these parameters are much more efficient than the interval type-2 FCM algorithm. The experiments carried out in this study show that the proposed typical characteristic-based type-2 FCM algorithm has an ability of detecting noise as well as assigning suitable membership degrees to the individual data.

关键词： clustering algorithms Fuzzy sets Noise measurement Phase change materials Uncertainty Linear programming Partitioning algorithms Cardinality center of gravity (COG) characteristics fuzzy clustering type-2 fuzzy sets (T2FS)

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A Novel Hammerstein Model for Nonlinear Networked Systems Based on an Interval Type-2 Fuzzy Takagi-Sugeno-Kang System

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第2期29卷 275-285页

作者： Khalifa, Tarek R. El-Nagar, Ahmad M. El-Brawany, Mohamed A. El-Araby, Essam A. G. El-Bardini, Mohammad Menoufia Univ Fac Elect Engn Shibin Al Kawm 32952 Egypt Menoufia Univ Dept Ind Elect & Control Engn Shibin Al Kawm 32952 Egypt

In this article, a novel Hammerstein structure is proposed for nonlinear networked systems based on an interval type-2 Takagi-Sugeno-Kang (IT2TSK) fuzzy system. The proposed approach is designed based on the general Hammerstein form, where an autoregressive moving average and an IT2TSK structures are designed as the linear energetic and nonlinear static components, respectively. The consequents of the nonlinear subsystem are characterized by a TSK-type system while the antecedents are characterized by interval type-2 fuzzy sets. The structure of the nonlinear subsystem is learned online based on the type-2 fuzzy clustering. Two new updating algorithms are introduced based on the Lyapunov theorem for learning the proposed model parameters and adaptive learning rates to assure the model stability and the parameter fast convergence. To illustrate the proposed model robustness, a test is performed under networked environment with time-varying delay and packet losses. The simulation results prove a higher performance for the proposed model than that of compared models.

关键词： Autoregressive processes Fuzzy systems Computational modeling Adaptation models Mathematical model clustering algorithms Nonlinear systems Fuzzy modeling Hammerstein model Lyapunov theorem nonlinear modeling type-2 fuzzy systems

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A novel baseline-free defect detection and localization method of welded steel plate based on reciprocity loss

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第2期32卷

作者： He, Tianhao Xing, Gailan Li, Yong Li, Qinfei Zhou, Shaoping East China Univ Sci & Technol Key Lab Pressure Syst & Safety Shanghai 200237 Peoples R China

In recent years, ultrasonic Lamb waves have been widely applied to the field of non-destructive testing of plate-like structures as they have outstanding advantages, such as low attenuation, high sensitivity, and wide detection range. Current studies about defect-detection of plate-like structures using Lamb waves mostly focus on non-weld plate-like structures, and defect-detection methods are based on baseline data. This paper proposes a novel baseline-free damage inspection method of welded plate-like structures, which is based on the principle of reciprocity loss and combines the OPTICS and K-means intelligent clustering algorithms to achieve accurate defect localization. In order to verify the location accuracy of the clustering defect localization algorithm, this paper performs comparative experiments between the ellipse imaging algorithm and the clustering algorithm, which use baseline data as health signals. The comparative experimental results show that the single-defect location accuracy of the clustering algorithm is greatly improved compared with the traditional ellipse algorithm. Moreover, in order to verify the validity and feasibility of the baseline-free method, this paper applies this method to obtain characteristic signals and combines the clustering algorithm to locate both single-defect and double-defects. The experimental result of baseline-free method shows that this method can successfully detect and locate multiple defects, which gets rid of the dependence of baseline data.

关键词： lamb wave weld plate-like structures reciprocity loss baseline-free clustering algorithms

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Extended Target Tracking With Multipath Detections, Terrain-Constrained Motion Model and Clutter

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第11期22卷 7056-7072页

作者： Liu, Ben Tharmarasa, Ratnasingham Jassemi, Rahim Brown, Daly Kirubarajan, Thia McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada Def Res & Dev Canada Ottawa ON K1N 1J8 Canada Gen Dynam Canada Ottawa ON K1S 1M9 Canada

To address the problem of extended target tracking (ETT) with measurement-origin uncertainty in a multipath environment with terrain-constrained motion model, a new generalized version of the standard probabilistic data association (PDA) filter, called MP-ET-PDA, based on random matrices (RM) is proposed in this paper. In the MP-ET-PDA filter, we assume that multipath detections and clutter are possible in the extended target tracking problem, which are prevalent in practical systems but barely addressed in the literature. Further, a clustering-aided MP-ET-PDA algorithm with a reduced computational complexity that makes use of the Variational Bayesian (VB) technique, called MP-ET-PDA-VB, is presented to provide near real-time processing capability in ETT problems with an uncertain multipath environment. In addition to using a constant velocity motion model, a new terrain-constrained motion model is presented for scenarios where terrain-following is required by motion-constrained autonomous vehicles. The posterior Cramer-Rao lower bound (PCRLB), which quantifies the best possible accuracy in realistic ETT problems with multipath detections and measurement-origin uncertainty, is derived as the benchmark for performance evaluation. Simulations results demonstrate the improved performance of the proposed algorithms.

关键词： Target tracking Force Measurement uncertainty Uncertainty clustering algorithms Clutter Computational modeling Extended target tracking probabilistic data association filter random matrices multipath detections variational Bayesian clustering

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