A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced

Health and Safety Reminders Via Augmented Reality Glasses

With the proliferation of personal electronic devices, the time individuals spend using their devices is an increasing concern. In particular, users may unconsciously spend too much time looking at a screen; their eyes may be too close to a screen, leading to eye strain; their posture may be suboptimal, leading to back or neck strain; etc. This disclosure describes techniques to leverage depth information detected with user permission via one or more sensors to determine when such situations occur and to provide suitable reminders to users, e.g., via augmented reality (AR) glasses

Image Based Navigation System for Pedestrians in an Indoor Environment

Indoor navigation systems provide means to guide pedestrians to their various destinations. While many tools that take advantage of the Global Positioning System (GPS) for outdoor navigation exists, their usefulness is limited to the availability of GPS signal reception, which is usually poor in indoor environments. In this research, we propose a method that employs only images for indoor pedestrian navigation. In the proposed method, a map of the indoor environment is first transformed into a graph model where features of indoor environment are attached to graph nodes and their distances represented by the graph edges. Feature images of initial and destination locations are provided by the pedestrian who needs to be guided. These images are fed into the created graph model and thereafter the Speeded-Up Robust Features (SURF) is then used to find a match to these images to discover their corresponding graph nodes. Graph nodes are identified in a manner that corresponds to pedestrian localized position and destination. Leveraging on these nodes in the graph, models are proposed to find the shortest path to user’s destination with instructions and graphical navigation path to enhance maneuverability. Experiment carried out on an indoor environment of the French South African Institute of Technology building, (Tshwane University of Technology) shows encouraging results

Dynamic small world network topology for particle swarm optimization

Abstract: A new particle optimization algorithm with dynamic topology is proposed based on a small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the particle swarm optimization(PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more eÆective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems

Niching particle swarm optimization based euclidean distance and hierarchical clustering for multimodal optimization

Abstract : Multimodal optimization is still one of the most challenging tasks in the evolutionary computation field, when multiple global and local optima need to be effectively and efficiently located. In this paper, a niching Particle Swarm Optimization (PSO) based Euclidean Distance and Hierarchical Clustering (EDHC) for multimodal optimization is proposed. This technique first uses the Euclidean distance based PSO algorithm to perform preliminarily search. In this phase, the particles are rapidly clustered around peaks. Secondly, hierarchical clustering is applied to identify and concentrate the particles distributed around each peak to finely search as a whole. Finally, a small world network topology is adopted in each niche to improve the exploitation ability of the algorithm. At the end of this paper, the proposed EDHC-PSO algorithm is applied to the Traveling Salesman Problems (TSP) after being discretized. The experiments demonstrate that the proposed method outperforms existing niching techniques on benchmark problems, and is effective for TSP

A Nationwide Study of Maternal Exposure To Ambient Ozone and Term Birth Weight In the United States

Background: Maternal exposure to ozone (O3) may cause systemic inflammation and oxidative stress and contribute to fetal growth restriction. We sought to estimate the association between maternal exposure to O3 and term birth weight and term small for gestational age (SGA) in the United States (US). Methods: We conducted a nationwide study including 2,179,040 live term singleton births that occurred across 453 populous counties in the contiguous US in 2002. Daily county-level concentrations of O3 data were estimated using a Bayesian fusion model. We used linear regression to estimate the association between O3 exposure and term birth weight and logistic regression to estimate the association between O3 exposure and term SGA during each trimester of the pregnancy and the entire pregnancy after adjusting for maternal characteristics, infant sex, season of conception, ambient temperature, county poverty rate, and census region. We additionally used distributed lag models to identify the critical exposure windows by estimating the monthly and weekly associations. Results: A 10 parts per billion (ppb) increase in O3 over the entire pregnancy was associated with a lower term birth weight (-7.6 g; 95 % CI: −8.8 g, −6.4 g) and increased risk of SGA (odds ratio = 1.030; 95 % CI: 1.020, 1.040). The identified critical exposure windows were the 13th- 25th and 32nd −37th gestational weeks for term birth weight and 13th- 25th for term SGA. We found the association was more pronounced among mothers who were non-Hispanic Black, unmarried, or had lower education level. Conclusions: Among US singleton term births, maternal exposure to O3 was associated with lower rates of fetal growth, and the 13th- 25th gestational weeks were the identified critical exposure windows

A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data

The stock price is a culmination of numerous factors that are not necessarily quantifiable and significantly affected by anomalies. The forecasting accuracy of stock prices is negatively affected by these anomalies. However, very few methods are available for detecting, modelling, and compensating for anomalies in financial time series given the critical roles of better management of funds and accurate forecasting of anomalies. Time series data are a data type that changes over a defined time interval. Each value in the data set has some relation to the previous values in the series. This attribute of time series data allows us to predict the values that will follow in the series. Typical prediction models are limited to following the patterns in the data set without being able to compensate for anomalous periods. This research will attempt to find a machine learning method to detect outliers and then compensate for these detections in the prediction made. This concept was previously unimplemented, and therefore, it will make use of theoretical work on market forecasting, outliers and their effects, and machine learning methods. The ideas implemented in the paper are based upon the efficient market hypothesis (EMH), in which the stock price reflects knowledge about the market. The EMH hypothesis cannot account for consumer sentiment towards a stock. This sentiment could produce anomalies in stock data that have a significant influence on the movement of the stock market. Therefore, the detection and compensation of outliers may improve the predictions made on stock movements. This paper proposes a deep learning method that consists of two sequential stages. The first stage is an outlier detection model based on a long short-term memory (LSTM) network auto-encoder that can determine if an outlier event has occurred and then create an associated value of this occurrence for the next stage. The second stage of the proposed method uses a higher-order neural network (HONN) model to make a prediction based on the output of the first stage and the stock time series data. Real stock data and standalone prediction models are used to validate this method. This method is superior at predicting stock time series data by compensating for outlier events. The improvement is quantifiable if the data set contains an adequate amount of anomalous periods. We may further apply the proposed method of compensating for outliers in combination with other financial time series prediction methods to offer further improvements and stability

A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data

The stock price is a culmination of numerous factors that are not necessarily quantifiable and significantly affected by anomalies. The forecasting accuracy of stock prices is negatively affected by these anomalies. However, very few methods are available for detecting, modelling, and compensating for anomalies in financial time series given the critical roles of better management of funds and accurate forecasting of anomalies. Time series data are a data type that changes over a defined time interval. Each value in the data set has some relation to the previous values in the series. This attribute of time series data allows us to predict the values that will follow in the series. Typical prediction models are limited to following the patterns in the data set without being able to compensate for anomalous periods. This research will attempt to find a machine learning method to detect outliers and then compensate for these detections in the prediction made. This concept was previously unimplemented, and therefore, it will make use of theoretical work on market forecasting, outliers and their effects, and machine learning methods. The ideas implemented in the paper are based upon the efficient market hypothesis (EMH), in which the stock price reflects knowledge about the market. The EMH hypothesis cannot account for consumer sentiment towards a stock. This sentiment could produce anomalies in stock data that have a significant influence on the movement of the stock market. Therefore, the detection and compensation of outliers may improve the predictions made on stock movements. This paper proposes a deep learning method that consists of two sequential stages. The first stage is an outlier detection model based on a long short-term memory (LSTM) network auto-encoder that can determine if an outlier event has occurred and then create an associated value of this occurrence for the next stage. The second stage of the proposed method uses a higher-order neural network (HONN) model to make a prediction based on the output of the first stage and the stock time series data. Real stock data and standalone prediction models are used to validate this method. This method is superior at predicting stock time series data by compensating for outlier events. The improvement is quantifiable if the data set contains an adequate amount of anomalous periods. We may further apply the proposed method of compensating for outliers in combination with other financial time series prediction methods to offer further improvements and stability

Appraising the impact of pressure control on leakage flow in water distribution networks

International audienceWater losses in Water Distribution Networks (WDNs) are inevitable. This is due to joints interconnections, ageing infrastructure and excessive pressure at lower demand. Pressure control has been showing promising results as a means of minimising water loss. Furthermore, it has been shown that pressure information at critical nodes is often adequate to ensure effective control in the system. In this work, a greedy algorithm for the identification of critical nodes is presented. An emulator for the WDN solution is put forward and used to simulate the dynamics of the WDN. A model-free control scheme based on reinforcement learning is used to interact with the proposed emulator to determine optimal pressure reducing valve settings based on the pressure information from the critical node. Results show that flows through the pipes and nodal pressure heads can be reduced using this scheme. The reduction in flows and nodal pressure leads to reduced leakage flows from the system. Moreover, the control scheme used in this work relies on the current operation of the system, unlike traditional machine learning methods that require prior knowledge about the system

A Survey of Pressure Control Approaches in Water Supply Systems

International audiencePressure control in water distribution networks (WDNs) provides an avenue for improving both their sustainability and reliability. The complexities of the networks make the problem more challenging as various situational operations must be accounted for to ensure that the entire system performs under recommended conditions. In general, this problem is addressed by the installation of pressure reducing valves (PRVs) in WDNs and determining their appropriate settings. Researchers have proposed the utilization of several control techniques. However, the limitations of both computational and financial resources have compelled the researchers to investigate the possibility of limiting the PRVs while ensuring their control is sufficient for the entire system. Several approaches have been put forward to mitigate this sub-problem of the pressure control problem. This paper presents a review of existing techniques to solve both the localization of PRVs and their control problems. It dwells briefly on the classification of these methods and subsequently highlights their merits and demerits. Despite the available literature, it can be noted that the solution methods are yet to be harmonized. As a result, various avenues of research areas are available. This paper further presents the possible research areas that could be exploited in this domain