An Overview of Moving Object Trajectory Compression Algorithms

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining

An Improved Local Community Detection Algorithm Using Selection Probability

In order to find the structure of local community more effectively, we propose an improved local community detection algorithm ILCDSP, which improves the node selection strategy, and sets selection probability value for every candidate node. ILCDSP assigns nodes with different selection probability values, which are equal to the degree of the nodes to be chosen. By this kind of strategy, the proposed algorithm can detect the local communities effectively, since it can ensure the best search direction and avoid the local optimal solution. Various experimental results on both synthetic and real networks demonstrate that the quality of the local communities detected by our algorithm is significantly superior to the state-of-the-art methods

Self-Contained In-The-Ear Devise to Deliver AAF

The design and operating characteristics of the first self-contained in-the-ear device to deliver altered auditory feedback is described for applications with those who stutter. The device incorporates a microdigital signal processor core that reproduces the high fidelity of unaided listening and auditory self-monitoring while at the same time delivering altered auditory feedback. Delayed auditory feedback and frequency-altered feedback signals in combination or isolation can be generated to the user in a cosmetically appealing custom in-the-canal and completely in-the-canal design. Programming of the device is achieved through a personal computer, interface, and fitting software. Researchers and clinicians interested in evaluating persons who stutter outside laboratory settings in a natural environment and persons who stutter looking for an alternative or adjunct to traditional therapy options are ideal candidates for this technology. In both instances an inconspicuous ear level alternative to traditional body worn devices with external microphones and earphones is offered

Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition

Mine hoists play a crucial role in vertical-shaft transportation, and one of the main causes of their faults is abnormal lifting load. However, direct measurement of the load value is difficult. Further, the original structure must be destroyed for sensor installation. To facilitate efficient and accurate monitoring of the lifting load of mine hoist, this paper presents a novel condition-monitoring method based on variational mode decomposition (VMD) and support vector machine (SVM) through vibration signal analysis. First, traditional empirical mode decomposition (EMD) is used to analyze the vibration signal collected by an acceleration sensor, and the number of obtained intrinsic mode functions (IMFs) is employed to set the VMD mode number. Second, the obtained vibration signal is processed by the parameterized VMD, and the useful IMFs of VMD are selected through correlation analysis for feature extraction. Third, the obtained features are used to train an SVM model, and the trained SVM is used to monitor the mine-hoist lifting load. In this study, experiments on an operated mine hoist are also conducted to verify the reliability and validity of the proposed method. The experimental results show that the proposed method can accurately identify the considered lifting load conditions

Further results on constructions of generalized bent Boolean functions

National Natural Science Foundation of China (Grant Nos. 61303263, 61309034)Fundamental Research Funds for the Central Universities (Grant No. 2015XKMS086)China Postdoctoral Science Foundation Funded Project (Grant No. 2015T80600)National Natural Science Foundation of China (Grant Nos. 61303263, 61309034)Fundamental Research Funds for the Central Universities (Grant No. 2015XKMS086)China Postdoctoral Science Foundation Funded Project (Grant No. 2015T80600

A Robust Fuzzy Kernel Clustering Algorithm

Traditional fuzzy kernel clustering methods does Iterative clustering in the original data space or in the feature space by mapping the samples into high-dimensional feature space through a kernel function These methods with normalized fuzzy degree of membership has weak robustness against noises and outliers, and lack of effective kernel parameter selection method. To overcome these problems, a robust kernel clustering algorithm is proposed to enhance the robustness by using typical parameter. Meawhile, a kernel function parameter optimization method under the unsupervised condition is also proposed in this paper. The experimental results show that the new algorithm is not only effective to the linear inseparable datasets with noisy data, but also more robust compared with other similar clustering algorithms and can obtain better clustering accuracy under noise jamming

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches

Multiple Kernel Spectral Regression for Dimensionality Reduction

Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR) solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL) into SR for dimensionality reduction. The proposed approach (termed MKL-SR) seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS) induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR) further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm

The Design and Implementation of Multiterminal Based Proactive Information Delivery System

Currently, the development of various communication terminal devices has greatly promoted people’s daily life, while information using efficiency with these devices decreases rapidly due to the information overload. To solve this problem, a multiterminal based proactive information delivery system (MPIDS) is designed and implemented in this paper. Firstly, users’ interests are computed comprehensively from the historical data, taking full consideration of users’ behaviors when visiting web pages. Secondly, a proactive information monitor service is introduced to monitor users’ data requirements and their interest changes, with which, user data are pushed to their online device automatically according to the strategies. Finally, a data self-adapter is given to encode and transform the data according to users’ online parameters and a series of data self-adaptive strategies. The experimental results show that MPIDS provides richly featured, secure, and robust personalized functions, reduces the running cost, and promotes the end-user experience and business efficiency