scholarly journals Detection of Outliers in a Time Series of Available Parking Spaces

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yanjie Ji ◽  
Dounan Tang ◽  
Weihong Guo ◽  
Phil T. Blythe ◽  
Gang Ren
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Phase Detection ◽  
Real World Data ◽  
Two Phase ◽  
Real Time Information ◽  
The City ◽  
Parking Facilities

With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrinsically distinct statistical features, a two-phase detection method is proposed whereby an improved density-based detection algorithm named “local entropy based weighted outlier detection” (EWOD) is also incorporated. Real-world data from parking facilities in the City of Newcastle upon Tyne was used to test the hypothesis. Thereafter, experimental tests were carried out for a comparative study in which the outlier detection performances of the two-phase detection method, statistic-based method, and traditional density-based method were compared and contrasted. The results showed that the proposed method can identify two different kinds of outliers simultaneously and can give a high identifying accuracy of 100% and 92.7% for the first and second types of outliers, respectively.

scholarly journals A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

2021 ◽  
Vol 11 (9) ◽  
pp. 3782
Author(s):  
Chu-Hui Lee ◽  
Chen-Wei Lin
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Detection Method ◽  
Phase Transfer ◽  
Recognition Task ◽  
Phase Detection ◽  
Target Domain ◽  
Two Phase ◽  
Detection Technology ◽  
Fashion Apparel

Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection.

scholarly journals A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Liying Wang ◽  
Lei Shi ◽  
Liancheng Xu ◽  
Peiyu Liu ◽  
Lindong Zhang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Detection Algorithm ◽  
Threshold Function ◽  
Detection Algorithms ◽  
Original Dataset ◽  
Data Clusters ◽  
High Computational Cost

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.

Two-phase edge outlier detection method for technology opportunity discovery

2017 ◽  
Vol 113 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Byunghoon Kim ◽  
Gianluca Gazzola ◽  
Jaekyung Yang ◽  
Jae-Min Lee ◽  
Byoung-Youl Coh ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Two Phase ◽  
Opportunity Discovery

scholarly journals Mining moving object data

2012 ◽  
Vol 2 (3) ◽  
Author(s):  
Jaroslav Zendulka ◽  
Martin Pešek
Keyword(s):  
Outlier Detection ◽  
Real World ◽  
Moving Objects ◽  
Detection Algorithm ◽  
Moving Object ◽  
Location Based Services ◽  
Data Sets ◽  
Real World Data ◽  
World Data ◽  
Trajectory Outlier Detection

AbstractCurrently many devices provide information about moving objects and location-based services that accumulate a huge volume of moving object data, including trajectories. This paper deals with two useful analysis tasks — mining moving object patterns and trajectory outlier detection. We also present our experience with the TOP-EYE trajectory outlier detection algorithm, which we applied to two real-world data sets.

scholarly journals Data Streams Oriented Outlier Detection Method: A Fast Minimal Infrequent Pattern Mining

2021 ◽  
Author(s):  
ZhongYu Zhou ◽  
DeChang Pi
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Pattern Mining ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Mining Method ◽  
Telemetry Data ◽  
Process Data ◽  
Mining Data Streams

Outlier detection is a common method for analyzing data streams. In the existing outlier detection methods, most of methods compute distance of points to solve certain specific outlier detection problems. However, these methods are computationally expensive and cannot process data streams quickly. The outlier detection method based on pattern mining resolves the aforementioned issues, but the existing methods are inefficient and cannot meet requirements of quickly mining data streams. In order to improve the efficiency of the method, a new outlier detection method is proposed in this paper. First, a fast minimal infrequent pattern mining method is proposed to mine the minimal infrequent pattern from data streams. Second, an efficient outlier detection algorithm based on minimal infrequent pattern is proposed for detecting the outliers in the data streams by mining minimal infrequent pattern. The algorithm proposed in this paper is demonstrated by real telemetry data of a satellite in orbit. The experimental results show that the proposed method not only can be applied to satellite outlier detection, but also is superior to the existing methods.

scholarly journals Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps

2020 ◽  
Vol 4 (4) ◽  
pp. 24
Author(s):  
Menglu Li ◽  
Rasha Kashef ◽  
Ahmed Ibrahim
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Self Organizing Maps ◽  
Business Outcomes ◽  
Online Transactions ◽  
Business Applications ◽  
Efficient Detection ◽  
Multi Level

Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection. The performance of existing outlier detection methods is limited by the pattern/behaviour of the dataset; these methods may not perform well without prior knowledge of the dataset. This paper proposes a multi-level outlier detection algorithm (MCOD) that uses multi-level unsupervised learning to cluster the data and discover outliers. The proposed detection method is tested on datasets in different fields with different sizes and dimensions. Experimental analysis has shown that the proposed MCOD algorithm has the ability to improving the outlier detection rate, as compared to the traditional anomaly detection methods. Enterprises and organizations can adopt the proposed MCOD algorithm to ensure a sustainable and efficient detection of frauds/outliers to increase profitability (and/or) to enhance business outcomes.

scholarly journals RODA: A Fast Outlier Detection Algorithm Supporting Multi-Queries

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 43271-43284
Author(s):  
Xite Wang ◽  
Jiafan Li ◽  
Mei Bai ◽  
Qian Ma
Keyword(s):  
Outlier Detection ◽  
Detection Algorithm

Research on the framework of university ideological and political education management system based on artificial intelligence

2021 ◽  
pp. 1-10
Author(s):  
Xuying Sun ◽  
Yu Zhang
Keyword(s):  
Artificial Intelligence ◽  
Political Theory ◽  
Outlier Detection ◽  
Management System ◽  
Detection Algorithm ◽  
Colleges And Universities ◽  
Ideological And Political Education ◽  
Political Education ◽  
Data Simulation ◽  
Artificial Intelligence Technology

The importance of the management of ideological and political theory courses in colleges and universities is objective to the importance of ideological and political theory courses. At present, the management of ideological and political theory courses in colleges and universities has big problems in both macro and micro aspects. This paper combines artificial intelligence technology to build an intelligent management system for ideological and political education in colleges and universities based on artificial intelligence, and conducts classroom supervision through intelligent recognition of student status. The KNN outlier detection algorithm based on KD-Tree is proposed to extract the state information of class students. Through data simulation, it can be known that the KD-KNN outlier detection algorithm proposed in this paper significantly improves the efficiency of the algorithm while ensuring the accuracy of the KNN algorithm classification. Through experimental research, it can be seen that the construction of this system not only clarifies the direction of management from a macro perspective, but also reveals specific methods of management from a micro perspective, and to a certain extent effectively solves the problems in the management of ideological and political theory courses in colleges and universities.

scholarly journals Investigation of Pressure Oscillation and Cavitation Characteristics for Submerged Self-Resonating Waterjet

2021 ◽  
Vol 11 (15) ◽  
pp. 6972
Author(s):  
Lihua Cui ◽  
Fei Ma ◽  
Tengfei Cai
Keyword(s):  
Sudden Change ◽  
Three Dimensional ◽  
Pressure Oscillation ◽  
Experimental Tests ◽  
Low Pressure ◽  
The Self ◽  
Two Phase ◽  
Cavitation Phenomenon ◽  
Pressure Zone ◽  
Unsteady Characteristics

The cavitation phenomenon of the self-resonating waterjet for the modulation of erosion characteristics is investigated in this paper. A three-dimensional computational fluid dynamics (CFD) model was developed to analyze the unsteady characteristics of the self-resonating jet. The numerical model employs the mixture two-phase model, coupling the realizable turbulence model and Schnerr–Sauer cavitation model. Collected data from experimental tests were used to validate the model. Results of numerical simulations and experimental data frequency bands obtained by the Fast Fourier transform (FFT) method were in very good agreement. For better understanding the physical phenomena, the velocity, the pressure distributions, and the cavitation characteristics were investigated. The obtained results show that the sudden change of the flow velocity at the outlet of the nozzle leads to the forms of the low-pressure zone. When the pressure at the low-pressure zone is lower than the vapor pressure, the cavitation occurs. The flow field structure of the waterjet can be directly perceived through simulation, which can provide theoretical support for realizing the modulation of the erosion characteristics, optimizing nozzle structure.

Sign in / Sign up

Export Citation Format

Share Document