scholarly journals An Ensemble Detection Model Using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 7877-7898 ◽  
Author(s):  
William Hurst ◽  
Casimiro Aday Curbelo Montanez ◽  
Nathan Shone ◽  
Dhiya Al-Jumeily
Keyword(s):  
Smart Cities ◽  
Smart Meter ◽  
Detection Model

A Systematized FMEA Practice for Magnet Relays

2010 ◽  
Vol 146-147 ◽  
pp. 757-769
Author(s):  
Ching Ming Cheng ◽  
Wen Fang Wu ◽  
Yao Hsu
Keyword(s):  
New Product Development ◽  
Failure Modes ◽  
New Product ◽  
Risk Priority Number ◽  
Detection Model ◽  
Regulation Function ◽  
Potential Failure ◽  
Weighting Rule ◽  
Rating Criterion

The Design Failure Modes and Effects Analysis (DFMEA) are generally applied to risk management of New Product Development (NPD) through standardization of potential failure modes and effect-ranking of rating criterion with failure modes. Typical 1 to 10 of effect-ranking are widely weighed the priority of classification, that framing effects and status quo senses might cause decision trap happening thus. The FMEA follows considerable indexes which are including Severity, Occurrence and Detection, and need be associated with difference between every two failures individually. However, we suspect that a more systematic construction of the analysis by which failure modes belong is necessary in order to make intellectual progress in this area. Two ways of such differentiation and construction are improvable effect-ranking and systematized indexes; here we resolve for attributes of failures with classification, maturity and experiance of indexes according to an existing rule. In Severity model, the larger differentiation is achieved by separating indexes to the classification of the Law & Regulation, Function and Cosmetic. Occurrence model has its characteristic a reliable ranking indexwhich assists decisionmakers to manage their venture. This is the model most closely associate with product maturity by grouping indexes to the new, extend and series product. Detection model offers a special perspective on cost; here the connections concerned with phase occasion of the review, verification and validation. Such differentiations will be proposed and mapped with the Life Cycle Profile (LCP) to systematize FMEA. Meanwhile, a more reasonable Risk Priority Number (RPN) with the new weighting rule will be worked out for effect-ranking and management system will be integrated systematiclly

A Lightweight Smart Meter Framework using a Scalable Blockchain for Smart Cities

2021 ◽  
Author(s):  
Joong-Lyul Lee ◽  
Prashanth BusiReddyGari ◽  
Brianna Thompson
Keyword(s):  
Smart Cities ◽  
Smart Meter

scholarly journals Deep Learning Classification of Building Types in Northern Cyprus

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mubarak Muhammad ◽  
Sertan Serte
Keyword(s):  
Deep Learning ◽  
Real Estate ◽  
Smart Cities ◽  
House Price ◽  
Training Dataset ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Northern Cyprus ◽  
Second Category

Among the areas where AI studies centered on developing models that provide real-time solutions for the real estate industry are real estate price forecasting, building age, and types and design of the building (villa, apartment, floor number). Nevertheless, within the ML sector, DL is an emerging region with an Interest increases every year. As a result, a growing number of DL research are in conferences and papers, models for real estate have begun to emerge. In this study, we present a deep learning method for classification of houses in Northern Cyprus using Convolutional neural network. This work proposes the use of Convolutional neural networks in the classification of houses images. The classification will be based on the house age, house price, number of floors in the house, house type i.e. Villa and Apartment. The first category is Villa versus Apartments class; based on the training dataset of 362 images the class result shows the overall accuracy of 96.40%. The second category is split into two classes according to age of the buildings, namely 0 to 5 years Apartments 6 to 10 years Apartments. This class is to classify the building based on their age and the result shows the accuracy of 87.42%. The third category is villa with roof versus Villa without roof apartments class which also shows the overall accuracy of 87.60%. The fourth category is Villa Price from 10,000 euro to 200,000 Versus Villa Price from 200,000 Euro to above and the result shows the accuracy of 81.84%. The last category consists of three classes namely 2 floor Apartment versus 3 floor Apartment, 2 floor Apartment versus 4 floor Apartment and 2 floor Apartment versus 5 floor Apartment which all shows the accuracy of 83.54%, 82.48% and 84.77% respectively. From the experiments carried out in this thesis and the results obtained we conclude that the main aims and objectives of this thesis which is to used Deep learning in Classification and detection of houses in Northern Cyprus and to test the performance of AlexNet for houses classification was successful. This study will be very significant in creation of smart cities and digitization of real estate sector as the world embrace the used of the vast power of Artificial Intelligence, machine learning and machine vision.

scholarly journals Prospects to create “smart cities” in Russia: Classification of problems and their solutions

2018 ◽  
Vol 13 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Anna Olegovna Veselova ◽  
◽  
Anna Nikolaevna Khatskelevich ◽  
Larisa Sergeevna Ezhova ◽  
◽  
...  
Keyword(s):  
Smart Cities

scholarly journals Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint

Smart Cities ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 1173-1186
Author(s):  
William Hurst ◽  
Bedir Tekinerdogan ◽  
Ben Kotze
Keyword(s):  
Social Class ◽  
Carbon Footprint ◽  
Employment Status ◽  
Smart Cities ◽  
Statistical Significance ◽  
End Users ◽  
Smart Meter ◽  
End User ◽  
Smart Meters ◽  
Smart Energy Systems

Carbon emission is a prominent issue, and smart urban solutions have the technological capabilities to implement change. The technologies for creating smart energy systems already exist, some of which are currently under wide deployment globally. By investing in energy efficiency solutions (such as the smart meter), research shows that the end-user is able to not only save money, but also reduce their household’s carbon footprint. Therefore, in this paper, the focus is on the end-user, and adopting a quantitative analysis of the perception of 1365 homes concerning the smart gas meter installation. The focus is on linking end-user attributes (age, education, social class and employment status) with their opinion on reducing energy, saving money, changing home behaviour and lowering carbon emissions. The results show that there is a statistical significance between certain attributes of end-users and their consideration of smart meters for making beneficial changes. In particular, the investigation demonstrates that the employment status, age and social class of the homeowner have statistical significance on the end-users’ variance; particularly when interested in reducing their bill and changing their behaviour around the home.

scholarly journals Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiran Feng ◽  
Xueheng Tao ◽  
Eung-Joo Lee
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multiple Objects ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

scholarly journals An Efficient Segmentation based Classification of Diabetic Retinopathy Identification using CLAHE with ResNet Model

2019 ◽  
Vol 8 (9) ◽  
pp. 2277-2283
Keyword(s):  
Diabetic Retinopathy ◽  
Histogram Equalization ◽  
Classification Model ◽  
Diabetic Patients ◽  
Detection Model ◽  
Adaptive Histogram Equalization ◽  
Active Population ◽  
Classifier Performance ◽  
Timely Treatment

Diabetic retinopathy (DR) is a widespread problem for diabetic patient and it has been a main reason for blindness in the active population. Several difficulties faced by diabetic patients because of DR can be eliminated by properly maintaining the blood glucose and by timely treatment. As the DR comes with different stages and varying difficulties, it is hard to DR and also it is time consuming. In this paper, we develop an automated segmentation based classification model for DR. Initially, the Contrast limited adaptive histogram equalization (CLAHE) is used for segmenting the images. Later, residual network (ResNet) is employed for classifying the images into different grades of DR. For experimental analysis, the dataset is derived from Kaggle website which is open source platform that attempts to build DR detection model. The highest classifier performance is attained by the presented model with the maximum accuracy of 83.78, sensitivity of 67.20 and specificity of 89.36 over compared models

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