Baicheng Yan

Bio: Baicheng Yan is an academic researcher from Beihang University. The author has contributed to research in topics: Bandwidth (computing) & Directory. The author has an hindex of 2, co-authored 12 publications receiving 23 citations.

N2DLOF: A New Local Density-Based Outlier Detection Approach for Scattered Data

Abstract: Since the Local Outlier Factor (LOF) was first proposed, there is a large family of approaches that is derived from it For the reason that the existing local outliers detection approaches only focus on the extent of overall separation between an object and its neighbors, and do not pay attention to the degree of dispersion between them, the precision of these approaches will be seriously affected in the scattered data sets for outlier detection In this paper, we redefine the local outliers by combining the degree of dispersion of the object and its neighbors, and propose a new local outlier detection approach (N2DLOF) Compared to conventional approaches, the outliers obtained by N2DLOF are more sensitive to the degree of anomaly of the scattered data sets Experiments show that our approach has a significant improvement on outlier detection precision in the case of scattered datasets with similar time complexity In short, we extend the ecosystem of the local outlier detection approaches from a new perspective

A self-tuning client-side metadata prefetching scheme for wide area network file systems

Abstract: Client-side metadata prefetching is commonly used in wide area network (WAN) file systems because it can effectively hide network latency. However, most existing prefetching approaches do not meet the various prefetching requirements of multiple workloads. They are usually optimized for only one specific workload and have no or harmful effects on other workloads. In this paper, we present a new self-tuning client-side metadata prefetching scheme that uses two different prefetching strategies and dynamically adapts to workload changes. It uses a directory-directed prefetching strategy to prefetch the related file metadata in the same directory, and a correlation-directed prefetching strategy to prefetch the related file metadata accessed across directories. A novel self-tuning mechanism is proposed to efficiently convert the prefetching strategy between directory-directed and correlation-directed prefetching. Experimental results using real system traces show that the hit ratio of the client-side cache can be significantly improved by our self-tuning client-side prefetching. With regards to the multi-workload concurrency scenario, our approach improves the hit ratios for the no-prefetching, directory-directed prefetching, variant probability graph algorithm, variant apriori algorithm, and variant semantic distance algorithm by up to 15.22%, 6.32%, 10.08%, 11.65%, and 10.73%, corresponding to 25.24%, 18.11%, 23.53%, 24.94%, and 24.19% reductions in the average access time, respectively.

Link fault detection method in HPC indirect network environment

Abstract: The invention provides a link fault detection method in a HPC indirect network environment. According to the link fault detection method based on link delay information measurement applied in the HPCindirect network environment, the link fault is detected by detection of anomaly of link delay information; and thus, a fault link in a network can be precisely determined within a relatively short time. The link fault detection method comprises the steps of: (a), querying HPC interconnection network routing information, so that the link composition of communication paths between nodes is obtained; (b), in combination with the link composition of each communication path, determining a key communication path set needing to perform delay measurement; (c), measuring delay information of a key path in parallel, and, on the basis of the information, solving delay information of all links in the whole network; and (d), judging whether the link fails or not according to the link delay information, and solving the link delay expected value in the network, wherein the delayed link, which has relatively large deviation with the value, is the fault link.

Ranking outliers using symmetric neighborhood relationship

Abstract: Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2,11]. However, when outliers are in the location where the density distributions in the neighborhood are significantly different, for example, in the case of objects from a sparse cluster close to a denser cluster, this may result in wrong estimation. To avoid this problem, here we propose a simple but effective measure on local outliers based on a symmetric neighborhood relationship. The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. To compute such local outliers efficiently, several mining algorithms are developed that detects top-n outliers based on our definition. A comprehensive performance evaluation and analysis shows that our methods are not only efficient in the computation but also more effective in ranking outliers.

Performance comparative of OpenCV Template Matching method on Jetson TX2 and Jetson Nano developer kits

Abstract: Template Matching is a widely used method for object detection in digital images, it requires great processing power since it is an exhaustive method that compares the intensity levels of a source image pixel-to-pixel with a template image that contains the object to identify. Nowadays there are dedicated embedded systems that provide high processing capabilities, such as the NVIDIA Jetson family. This work presents the experimentation and the performance comparison between the Jetson Nano and Jetson TX2 development kits, when implementing the Template Matching method, in order to get an evaluation criterion to select one of them in image processing projects. It was carried out to six images with different sizes and two variants in terms of the size of the template image. The processing times for the sequential implementation using the CPUs and the parallel implementation with the GPUs were obtained quantitatively. It was observed that the processing times using the parallel versions on average doubled those of the sequential versions and that the Jetson TX2 exceeded the Jetson Nano in execution speeds.

An Efficient Density-Based Local Outlier Detection Approach for Scattered Data

Abstract: After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In addition, the outlier data occupy a relatively small amount in the dataset, but the existing approaches need to perform local outlier factor calculation on all data during the outlier detection, which greatly reduces the efficiency of the algorithms. In this paper, we redefine a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors. And then, we propose a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. Finally, an efficient local outlier detection approach named as efficient density-based local outlier detection for scattered data (E2DLOS) is proposed based on the LDC and RCMLQ. The RCMLQ greatly reduces the amount of data that needs to be quantified for local outlier factor and the LDC is more sensitive to the degree of anomaly of the scattered datasets, and so the E2DLOS improves the existing local outlier detection approaches in time efficiency and detection accuracy. Experiments show that the LDC can better reflect the true abnormal situations of the data for the scattered datasets. And the RCMLQ can be used in parallel with the traditional methods of improving the efficiency of the nearest neighbor search, which can further improve the efficiency of the E2DLOS algorithm by about 16%.

Anomaly detection for condition monitoring data using auxiliary feature vector and density-based clustering

Abstract: High-quality condition monitoring data can provide vital information on power equipment condition assessment and fault diagnosis. However, data quality is difficult to guarantee because valid and invalid anomalies, and different normal data patterns inevitably occur in datasets. This study presents a method for anomaly detection based on auxiliary feature vector and density-based spatial clustering of applications with noise (DBSCAN). The auxiliary feature vectors of each condition variable are constructed for clustering to recognise normal data patterns and different types of anomalies. Furthermore, a heuristic method based on the ‘number of clusters–Eps’ curve is proposed for the parameter selection of DBSCAN in an unsupervised setting. Different application examples are implemented on data of dissolved gas content in transformer oil. Compared with state-of-the-art anomaly detection techniques, the proposed method shows an ability to identify and distinguish normal data patterns and valid and invalid anomalies accurately, provided that the condition monitoring data satisfy the assumption of stationarity.