scholarly journals Development of Automated Top-Down Construction System for Low-rise Building Structures

2020 ◽  
Vol 1 (1) ◽  
pp. 22-33
Author(s):  
Aparna Harichandran ◽  
Benny Raphael ◽  
Abhijit Mukherjee
Keyword(s):  
Monitoring System ◽  
Motor System ◽  
Identification Accuracy ◽  
Automated System ◽  
Support Vector ◽  
Building Structures ◽  
Structural System ◽  
Hydraulic Motor ◽  
Top Down ◽  
Construction System

Automation is the best solution for achieving high productivity and quality in the construction industry at reduced cost and time. The main objective of this study is to develop an economical automated construction system (ACS) for low-rise buildings. The incremental development of the construction system and the structural system through different versions of laboratory prototypes are described in this paper. These ACS prototypes adopt a top-down construction method. This method involves the building of the structural system step by step from the top floor to the bottom floor by connecting and lifting structural modules. ACS prototype 1 consist of wooden structural modules and electric motor system. ACS prototype 2 has a highly automated custom designed hydraulic motor system to construct steel structural frame. ACS prototype 3 is a partially automated system where the steel structural modules are connected manually. These prototypes were evaluated on the basis of function, cost and efficiency of operations. Based on overall performance, ACS prototype 3 is identified as the best economical option for the construction of low-rise buildings. When the speed of construction is more important than cost, the ACS prototype 2 is the apt solution. This paper describes the challenges in developing an ACS and the criteria to evaluate its performance. It also includes a preliminary framework for the development of an automated construction monitoring system and its experimental evaluation. This machine learning-based framework is to identify the operations of ACS from sensor measurements using Support Vector Machines. Most of the operations are identified reasonably well and the best identification accuracy is 96%. The future studies are focusing on to improve the accuracy of operation identification, further development of the monitoring system and the ACS for actual implementation in construction sites.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Alexander Knyshov ◽  
Samantha Hoang ◽  
Christiane Weirauch
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Training Image ◽  
Species Level ◽  
Identification Accuracy ◽  
Support Vector ◽  
Test Case ◽  
Learning Approaches ◽  
Lower Accuracy ◽  
Neural Network Classifiers

Abstract Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.

Action Recognition with Spatiotemporal Analysis and Support Vector Machine for Elderly Monitoring System

2021 ◽  
Author(s):  
Mahaputra Ilham Awal ◽  
Luqmanul Hakim Iksan ◽  
Rizky Zull Fhamy ◽  
Dwi Kurnia Basuki ◽  
Sritrusta Sukaridhoto ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Monitoring System ◽  
Action Recognition ◽  
Spatiotemporal Analysis ◽  
Support Vector

scholarly journals Flexible Impedimetric Electronic Nose for High-Accurate Determination of Individual Volatile Organic Compounds by Tuning the Graphene Sensitive Properties

Chemosensors ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 360
Author(s):  
Tianqi Lu ◽  
Ammar Al-Hamry ◽  
José Mauricio Rosolen ◽  
Zheng Hu ◽  
Junfeng Hao ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Accurate Determination ◽  
Identification Accuracy ◽  
Structural Defects ◽  
Support Vector ◽  
Volatile Organic ◽  
Variable Sensitivity ◽  
Relative Change

We investigated functionalized graphene materials to create highly sensitive sensors for volatile organic compounds (VOCs) such as formaldehyde, methanol, ethanol, acetone, and isopropanol. First, we prepared VOC-sensitive films consisting of mechanically exfoliated graphene (eG) and chemical graphene oxide (GO), which have different concentrations of structural defects. We deposited the films on silver interdigitated electrodes on Kapton substrate and submitted them to thermal treatment. Next, we measured the sensitive properties of the resulting sensors towards specific VOCs by impedance spectroscopy. We obtained the eG- and GO-based electronic nose composed of two eG films- and four GO film-based sensors with variable sensitivity to individual VOCs. The smallest relative change in impedance was 5% for the sensor based on eG film annealed at 180 °C toward 10 ppm formaldehyde, whereas the highest relative change was 257% for the sensor based on two-layers deposited GO film annealed at 200 °C toward 80 ppm ethanol. At 10 ppm VOC, the GO film-based sensors were sensitive enough to distinguish between individual VOCs, which implied excellent selectivity, as confirmed by Principle Component Analysis (PCA). According to a PCA-Support Vector Machine-based signal processing method, the electronic nose provided identification accuracy of 100% for individual VOCs. The proposed electronic nose can be used to detect multiple VOCs selectively because each sensor is sensitive to VOCs and has significant cross-selectivity to others.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

scholarly journals PSO-SVM hybrid system for melanoma detection from histo-pathological images

2019 ◽  
Vol 9 (4) ◽  
pp. 2941
Author(s):  
Maen Takruri ◽  
Mohamed Khaled Abu Mahmoud ◽  
Adel Al-Jumaily
Keyword(s):  
Hybrid System ◽  
Wavelet Packet ◽  
Automated System ◽  
Support Vector ◽  
Svm Classifier ◽  
Pathology Laboratory ◽  
Swarm Optimization ◽  
Image Histogram ◽  
Melanoma Detection ◽  
Occurrence Matrix

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier.   The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.

scholarly journals Implementation of an Automated System in the Measurement of Temperature in Broken Dynamic Equipment

2020 ◽  
Vol 9 (3) ◽  
pp. 180-183
Keyword(s):  
Temperature Measurement ◽  
Monitoring System ◽  
Temperature Control ◽  
Theoretical Basis ◽  
Lower Cost ◽  
Automated System ◽  
Cost System ◽  
Arduino Uno

This article presents a titration project describing the implementation in the rotodynamic equipment of an economical automated temperature module, as a preventive solution for future failures caused by the lack of analysis in the increase or decrease in temperature. The project is currently contextualized in the area of industry, first, providing background to frame the importance of temperature control and measurement and also know what its evolution has been like. Immediately focuses on explaining the theoretical basis for giving context to the reader. For the purpose of detecting the increase or decrease of heat in machinery by implementing a monitoring system. The development of the project is based on the use of an LM35 transistor that connected to an Arduino Uno through various cables, will display the temperature measurement and make interface of the obtained results that will be reflected in a 2x16 LCD screen. The project is applied in a prototype bench in three key parts of the pulley, and in the two bearings to make the simulations, then perform corresponding tests and check that theory. A simple and lower cost system, but above all efficient that meets the expectations of the problem presented.

scholarly journals Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hong-Chan Chang ◽  
Shang-Chih Lin ◽  
Cheng-Chien Kuo ◽  
Hao-Ping Yu
Keyword(s):  
Support Vector Machine ◽  
Information System ◽  
Monitoring System ◽  
Solar Power ◽  
Video Data ◽  
Failure Diagnosis ◽  
Support Vector ◽  
Management Platform ◽  
System A ◽  
Cloud Monitoring

This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P) streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM) theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.

The Intelligent Monitoring System

1990 ◽  
Vol 80 (6B) ◽  
pp. 1833-1851 ◽  
Author(s):  
Thomas C. Bache ◽  
Steven R. Bratt ◽  
James Wang ◽  
Robert M. Fung ◽  
Cris Kobryn ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Monitoring System ◽  
Database Management System ◽  
Automated System ◽  
Seismic Events ◽  
Process Data ◽  
Intelligent Monitoring ◽  
Seismic Arrays ◽  
Interactive Analysis ◽  
Knowledge Based

Abstract The Intelligent Monitoring System (IMS) is a computer system for processing data from seismic arrays and simpler stations to detect, locate, and identify seismic events. The first operational version processes data from two high-frequency arrays (NORESS and ARCESS) in Norway. The IMS computers and functions are distributed between the NORSAR Data Analysis Center (NDAC) near Oslo and the Center for Seismic Studies (Center) in Arlington, Virginia. The IMS modules at NDAC automatically retrieve data from a disk buffer, detect signals, compute signal attributes (amplitude, slowness, azimuth, polarization, etc.), and store them in a commercial relational database management system (DBMS). IMS makes scheduled (e.g., hourly) transfers of the data to a separate DBMS at the Center. Arrival of new data automatically initiates a “knowledge-based system (KBS)” that interprets these data to locate and identify (earthquake, mine blast, etc.) seismic events. This KBS uses general and area-specific seismological knowledge represented in rules and procedures. For each event, unprocessed data segments (e.g., 7 min for regional events) are retrieved from NDAC for subsequent display and analyst review. The interactive analysis modules include integrated waveform and map display/manipulation tools for efficient analyst validation or correction of the solutions produced by the automated system. Another KBS compares the analyst and automatic solutions to mark overruled elements of the knowledge base. Performance analysis statistics guide subsequent changes to the knowledge base so it improves with experience. The IMS is implemented on networked Sun workstations, with a 56 kbps satellite link bridging the NDAC and Center computer networks. The software architecture is modular and distributed, with processes communicating by messages and sharing data via the DBMS. The IMS processing requirements are easily met with major processes (i.e., signal processing, KBS, and DBMS) on separate Sun 4/2xx workstations. This architecture facilitates expansion in functionality and number of stations. The first version was operated continuously for 8 weeks in late-1989. The Center functions were then transferred to NDAC for subsequent operation. Later versions will be distributed among NDAC, Scripps/IGPP (San Diego), and the Center to process data from many stations and arrays. The IMS design is ambitious in its integration of many new computer technologies, but the operational performance of the first version demonstrates its validity. Thus, IMS provides a new generation of automated seismic event monitoring capability.

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

2021 ◽  
Vol 163 (A3) ◽  
Author(s):  
B Shabani ◽  
J Ali-Lavroff ◽  
D S Holloway ◽  
S Penev ◽  
D Dessi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Probability Distributions ◽  
Feature Space ◽  
Support Vector ◽  
Acceleration Threshold ◽  
Vector Machines ◽  
Structural Responses ◽  
As Stress

An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering current technology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has been developed and installed on a 111 m wave piercing catamaran (Hull 091) to remotely monitor the ship kinematics and hull structural responses. Parallel to that, an existing dataset of a similar vessel (Hull 061) was analysed using unsupervised and supervised learning models; these were found to be beneficial for the classification of bow entry events according to key kinematic parameters. A comparison of different algorithms including linear support vector machines, naïve Bayes and decision tree for the bow entry classification were conducted. In addition, using empirical probability distributions, the likelihood of wet-deck slamming was estimated given a vertical bow acceleration threshold of 1  in head seas, clustering the feature space with the approximate probabilities of 0.001, 0.030 and 0.25.

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