scholarly journals Detection of water quality failure events at treatment works using a hybrid two-stage method with CUSUM and random forest algorithms

2021 ◽  
Author(s):  
Gerald Riss ◽  
Michele Romano ◽  
Fayyaz Ali Memon ◽  
Zoran Kapelan
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
New Method ◽  
Machine Learning Techniques ◽  
False Alarms ◽  
Real World Data ◽  
Statistical Process ◽  
Quality Signals

Abstract Near real-time event detection is crucial for water utilities to be able to detect failure events in their water treatment works (WTW) quickly and efficiently. This paper presents a new method for an automated, near real-time recognition of failure events at WTWs by the application of combined statistical process control and machine learning techniques. The resulting novel hybrid CUSUM event recognition system (HC-ERS) uses two distinct detection methodologies: one for fault detection at the level of individual water quality signals and the second for the recognition of faulty processes at the WTW level. HC-ERS was tested and validated on historical failure events at a real-life UK WTW. The new methodology proved to be effective in the detection of failure events, achieving a high true detection rate of 82% combined with a low false alarm rate (average 0.3 false alarms per week), reaching a peak F1 score of 84% as measure of accuracy. The new method also demonstrated higher accuracy compared to the CANARY detection methodology. When applied to real-world data, the HC-ERS method showed the capability to detect faulty processes at WTW automatically and reliably, and hence potential for practical application in the water industry.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

scholarly journals Real-Time Water Quality Monitoring with Chemical Sensors

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3432 ◽  
Author(s):  
Irina Yaroshenko ◽  
Dmitry Kirsanov ◽  
Monika Marjanovic ◽  
Peter A. Lieberzeit ◽  
Olga Korostynska ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Limit Of Detection ◽  
Real Life ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Materials Integration ◽  
Operation Stability ◽  
Higher Sensitivity ◽  
Chemical Materials

Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described.

scholarly journals FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network

2021 ◽  
Vol 38 (5) ◽  
pp. 1495-1501
Author(s):  
Hui Huang ◽  
Zhe Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
Real Time ◽  
Large Scale ◽  
Real Life ◽  
Detection Algorithm ◽  
False Alarms ◽  
License Plate ◽  
Embedded Devices ◽  
License Plate Detection

The license plate detection technology has been widely applied in our daily life, but it encounters many challenges when performing license plate detection tasks in special scenarios. In this paper, a license plate detection algorithm is proposed for the problem of license plate detection, and an efficient false alarm filter algorithm, namely the FAFNet (False-Alarm Filter Network) is proposed for solving the problem of false alarms in license plate location scenarios in China. At first, this paper adopted the YOLOv5 target detection algorithm to detect license plates, and used the FAFNet to re-identify the images to avoid false detection. FAFNet is a lightweight convolutional neural network (CNN) that can solve the false alarm problem of real-time license plate recognition on embedded devices, and its performance is good. Next, this paper proposed a model generalization method for the purpose of making the proposed FAFNet be applicable to the license plate false alarm scenarios in other countries without the need to re-train the model. Then, this paper built a large-scale false alarm filter dataset, all samples in the dataset came from the industries and contained a variety of complex real-life scenarios. At last, experiments were conducted and the results showed that, the proposed FAFNet can achieve high-accuracy false alarm filtering and can run in real-time on embedded devices.

scholarly journals Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data

2020 ◽  
Vol 10 (18) ◽  
pp. 6270
Author(s):  
Erik Sonnleitner ◽  
Oliver Barth ◽  
Alexander Palmanshofer ◽  
Marc Kurz
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Road Traffic ◽  
Hybrid Approach ◽  
Real Life ◽  
Video Data ◽  
Machine Learning Techniques ◽  
Traffic Measurement ◽  
Learning Techniques ◽  
Toll Station

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%.

Real-Time Human Action Recognition System Using Depth Map Sequences

2013 ◽  
Vol 760-762 ◽  
pp. 1647-1651
Author(s):  
Yan Li Li ◽  
Gui Jin Wang ◽  
Xing Gang Lin ◽  
Guang Cheng ◽  
Li He
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
High Speed ◽  
Real Life ◽  
Depth Map ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Shape Context ◽  
Scale Invariant

This work presents a real-time human action recognition system that uses depth map sequence as input. The system contains the segmentation of human, the action modeling based on 3D shape context and the action graph algorithm. We effectively solve the problem of segmenting human from complex and cluttered scenes by combing a novel quadtree split-and-merge method and the codebook background modeling algorithm. We aims at recognizing actions that are used in games and interactions, especially complex actions that contain foot motion and body heave. By expanding the shape context descriptor into 3D space, we obtain translation and scale invariant features and get rid of normalization error, which is a common problem of real-life applications. Experiments in various scenarios demonstrate the high speed and excellent performance of our procedure.

scholarly journals Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 108 ◽  
Author(s):  
Georgios Paraskevopoulos ◽  
Evaggelos Spyrou ◽  
Dimitrios Sgouropoulos ◽  
Theodoros Giannakopoulos ◽  
Phivos Mylonas
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Data Set ◽  
3D Space ◽  
Novel Approach ◽  
Learning Techniques ◽  
Real Life Data ◽  
Art Research

In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classification approaches prove the effectiveness and the potential of our approach both with the proposed dataset and also compared to state-of-the-art research works.

Passive infrared detector used for detection of very slowly moving of crawling people

2008 ◽  
Vol 16 (3) ◽  
Author(s):  
M. Kastek ◽  
T. Sosnowski ◽  
T. Piątkowski
Keyword(s):  
Real Time ◽  
High Probability ◽  
Moving Objects ◽  
Infrared Detector ◽  
Probability Of Detection ◽  
New Method ◽  
False Alarms ◽  
Ir Detectors ◽  
Detection Range ◽  
Efficient Detection

AbstractThe paper presents construction and principle of operation of passive IR detectors (PIR detectors) of a large detection range. Important virtue of these detectors is highly efficient detection of slowly moving or crawling people. The described here PIR detector detects crawling people at the distance of 140 m. To ensure high probability of detection of slowly moving objects, new method of signals analysis was applied. On the basis of the carried out real-time measurements, both probability of detection and false alarms were determined.

scholarly journals Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things

2019 ◽  
Vol 8 (5S3) ◽  
pp. 53-57
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Random Forest ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Learning Techniques

IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.

scholarly journals Detecting Human and Classification of Gender using Facial Images MSIFT Features Based GSVM

2019 ◽  
Vol 8 (3) ◽  
pp. 1466-1471 ◽  
Keyword(s):  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Human Detection ◽  
Support Vector ◽  
Time Data ◽  
Processing Application ◽  
Wide Range ◽  
Human Image

Classification of gender using face recognition system is an essential concept for different types of applications in human-computer interaction and computer-aided related applications. It defines a wide range of features from human images to detect male, female and others using real-time data. There are different machine learning approaches were implemented to classify gender and also detects other images during the classification phase, which are not humans based on features extracted from human images datasets. All these existing techniques mostly depend on controlled conditions like features and other representations of the human image. Because of significant and uncertain variations of a particular image, it may be a challenging task in gender classification for real-time image processing application, whether it is male, female and others. So that in this document, we propose a Human detection and Face based gender Recognition System (HDFGR); to investigate male or female classification on real life faces using real world face databases. Our proposed approach consists Multi-Scale Invariant Feature Transform (MSIFT) to describes faces and Gaussian distance-based support vector machine (GSVM) classifier is used to classify gender and objects, i.e. male, female and other from features extracted human image datasets. We obtain an experimental performance of 98.7% by applying DSVM with boosted MSIFT features. Our proposed approach gives better classification accuracy and other performance parameters compared to different existing approaches with benchmark and evaluation of possible databases.

Sign in / Sign up

Export Citation Format

Share Document