Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine

Vibration technique in a machine condition monitoring provides useful reliable information, bringing significant cost benefits to industry. By comparing the signals of a machine running in normal and faulty conditions, detection of defected journal bearings is possible. This paper presents fault diagnosis of a journal bearing based on vibration analysis using three classifiers: Fisher Linear Discriminant (FLD), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The frequency-domain vibration signals of an internal combustion engine with intact and defective main journal bearings were obtained. 30 features were extracted by using statistical and vibration parameters. These features were used as inputs to the classifiers. Two different solution methods - variable K value and RBF kernel width (σ) were applied for FLD, KNN and SVM, respectively, in order to achieve the best accuracy. Finally, performance of the three classifiers was calculated in journal bearing fault diagnosis. The results demonstrated that the performance of SVM was significantly better in comparison to FLD and KNN. Also the results confirmed the potential of this procedure in fault diagnosis of journal bearings

Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model.

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agent-based model can approximately reproduce the patterns of migration involving overnight stays

Finding regions of interest using location based social media

The discovery of regions of interest in city groups is increasingly important in recent years. In this light, we propose and investigate a novel problem called Region Discovery query (RD query) that finds regions of interest with respect to a user's current geographic location. Given a set of spatial objects O and a query location q, if a circular region ω is with high spatial-object density and is spatially close to q, it is returned by the query and is recommended to users. This type of query can bring significant benefit to users in many useful applications such as trip planning and region recommendation. The RD query faces a big challenge: how to prune the search space in the spatial and density domains. To overcome the challenge and process the RD query efficiently, we propose a novel collaboration search method and we define a pair of bounds to prune the search space effectively. The performance of the RD query is studied by extensive experiments on real and synthetic spatial data

Efficient query processing over uncertain road networks

One of the fundamental problems on spatial road networks has been the shortest traveling time query, with applications such as location-based services (LBS) and trip planning. Algorithms have been made for the shortest time queries in deterministic road networks, in which vertices and edges are known with certainty. Emerging technologies are available and make it easier to acquire information about the traffic. In this paper, we consider uncertain road networks, in which speeds of vehicles are imprecise and probabilistic. We will focus on one important query type, continuous probabilistic shortest traveling time query (CPSTTQ), which retrieves sets of objects that have the smallest traveling time to a moving query point q from point s to point e on road networks with high confidences. We propose effective pruning methods to prune the search space of our CPSTTQ query, and design an efficient query procedure to answer CPSTTQ via an index structure

Best point detour query in road networks

A point detour is a temporary deviation from a user preferred path P (not necessarily a shortest network path) for visiting a data point such as a supermarket or McDonald's. The goodness of a point detour can be measured by the additional traveling introduced, called point detour cost or simply detour cost. Given a preferred path to be traveling on, Best Point Detour (BPD) query aims to identify the point detour with the minimum detour cost. This problem can be frequently found in our daily life but is less studied. In this work, the efficient processing of BPD query is investigated with support of devised optimization techniques. Furthermore, we investigate continuous-BPD query with target at the scenario where the path to be traveling on continuously changes when a user is moving to the destination along the preferred path. The challenge of continuous-BPD query lies in finding a set of update locations which split P into partitions. In the same partition, the user has the same BPD. We process continuous-BPD query by running BPD queries in a deliberately planned strategy. The efficiency study reveals that the number of BPD queries executed is optimal. The efficiency of BPD query and continuous-BPD query processing has been verified by extensive experiments

Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories

This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing. 1

Spatiotemporal Indexing With the M-Tree

Modern GIS applications for transportation and defense often require the ability to store the evolving positions of a large number of objects as they are observed in motion, and to support queries on this spatiotemporal data in real time. Because the M-Tree has been proven as an index for spatial network databases, we have selected it to be enhanced as a spatiotemporal index. We present modifications to the tree which allow trajectory reconstruction with fast insert performance and modifications which allow the tree to be built with awareness of the spatial locality of reference in spatiotemporal data