Predicting the Smart Meters Life Cycle Based on the Analysis of Correlation Coefficient

2013 ◽  
Vol 313-314 ◽  
pp. 629-633 ◽  
Author(s):  
Jin Shuo Liu ◽  
Chong Li ◽  
Shao Teng Li ◽  
Hong Ming Hong ◽  
Kun Hu
Keyword(s):  
Neural Network ◽  
Life Cycle ◽  
Production Process ◽  
Working Conditions ◽  
Failure Criteria ◽  
Smart Meter ◽  
Limited Data ◽  
Smart Meters ◽  
Design Production ◽  
The Cost

Since smart meters work at the long status without any supervision, their confidence is very important. This paper proposes an algorithm model of analyzing the related attributes with the running breakdown information of the smart meter, which can be utilized to predicate reliability of the life cycle of the smart meter. Because the limited data, the model mainly consider the attributes: the producer, application unit, and the failure information, without considering function, failure criteria, complexity, design, production process, working conditions, and the cost of installment and maintenances etc. The model utilizes the algorithm of neural network as the aid, and use producer, application unit, and failure info as the main attributes. Through the experiments of all the 16000 data, the fault predicting rate is 1%, which can prove the practicality.

An Application of Estimate Expert System Technology for Burrless Rubber Mould Manufacturing Process

2011 ◽  
Vol 308-310 ◽  
pp. 1732-1739
Author(s):  
Sang Bong Park
Keyword(s):  
Expert System ◽  
Production Process ◽  
Cost Estimation ◽  
System Technology ◽  
Moulding Process ◽  
Total Process ◽  
Design Production ◽  
The Subject ◽  
The Cost ◽  
Rubber Product

Rubber products have durable, lightweight, and shockproof properties, which allow them to be used in various industries. As demand on products with elaborate shape and parts and minimized occurrence of burrs has increased in high-precision industries, particularly in IT, BT, and NT, moulding technology becomes an essential technology to develop core objects composing frames for assembly and elementary parts that produce ultra small machines and basic components of MEMS and NEMS. Despite the technological demand, rubber-related moulding technology has not been standardized, and its total process – shaping, design, production, and test – has not been systemized, which results in low productivity in rubber products. This study is to standardize and systematize the rubber product production process by evaluating the cost estimation of moulding production process and to improve occurring problems during the process. The subject of the study was burrless rubber product moulding process.

IMPLEMENTASI JOB ORDER COSTING METHOD DALAM PERHITUNGAN HARGA POKOK PRODUKSI MAP

2018 ◽  
Vol 4 (2) ◽  
pp. 43-55
Author(s):  
Ika Yulianti ◽  
Endah Masrunik ◽  
Anam Miftakhul Huda ◽  
Diana Elvianita
Keyword(s):  
Production Process ◽  
Quantitative Data ◽  
Production Cost ◽  
Qualitative Data ◽  
Cost Data ◽  
Method Calculation ◽  
Cost Of Production ◽  
The Right ◽  
The Cost

This study aims to find a comparison of the calculation of the cost of goods manufactured in the CV. Mitra Setia Blitar uses the company's method and uses the Job Order Costing (JOC) method. The method used in this study is quantitative. The types of data used are quantitative and qualitative. Quantitative data is in the form of map production cost data while qualitative data is in the form of information about map production process. The result of calculating the cost of production of the map between the two methods results in a difference of Rp. 306. Calculation using the company method is more expensive than using the Job Order Costing method. Calculation of cost of goods manufactured using the company method is Rp. 2,205,000, - or Rp. 2,205, - each unit. While using the Job Order Costing (JOC) method is Rp. 1,899,000, - or Rp 1,899, - each unit. So that the right method used in calculating the cost of production is the Job Order Costing (JOC) method

Evaluation Of The Functional Of Space-Planning Arrangement Of A Building

2019 ◽  
pp. 4-9
Keyword(s):  
Numerical Simulation ◽  
Life Cycle ◽  
Initial Data ◽  
Objective Assessment ◽  
Objective Evaluation ◽  
Point Of View ◽  
Shopping Centers ◽  
Space Planning ◽  
Planning Decisions ◽  
The Cost

The article focuses on the problem of the lack of objective evaluation of space-planning arrangement of buildings as a creative approach of the architect to the performing of functional tasks by the object. It is proposed to create a methodology for assessing the functional of space-planning solutions of buildings on the basis of numerical simulation of functional processes using the theory of human flows. There is a description of the prospects of using this method, which makes it possible to increase the coefficient of compactness, materials and works saving, more efficient use of space, reduce the cost of the life cycle of the building, save human forces and time to implement the functional of the building. The necessary initial data for modeling on the example of shopping and shopping-entertainment centers are considered. There are three main tasks for algorithmization of the functional of shopping centers. The conclusion is made about necessity of development of a method for objective assessment of buildings from the point of view of ergonomics of space-planning decisions based on the study of human behavior in buildings of different purposes.

The chronicle of COVID-19: possible strategies to curb the pandemic

2020 ◽  
Vol 27 ◽  
Author(s):  
Rajesh Kumar ◽  
Seetha Harilal ◽  
Abdullah G. Al-Sehemi ◽  
Githa Elizabeth Mathew ◽  
Simone Carradori ◽  
...  
Keyword(s):  
Life Cycle ◽  
Research And Development ◽  
Vaccine Development ◽  
Therapeutic Strategies ◽  
Antiviral Drugs ◽  
Limited Data ◽  
Social Distancing ◽  
The World ◽  
Novel Virus ◽  
Day By Day

: COVID-19, an epidemic that emerged in Wuhan, has become a pandemic affecting worldwide and is in a rapidly evolving condition. Day by day, the confirmed cases and deaths are increasing many folds. SARS-CoV-2 is a novel virus; therefore, limited data are available to curb the disease. Epidemiological approaches, isolation, quarantine, social distancing, lockdown, and curfew are being employed to halt the spread of the disease. Individual and joint efforts all over the world are producing a wealth of data and information which are expected to produce therapeutic strategies against COVID-19. Current research focuses on the utilization of antiviral drugs, repurposing strategies, vaccine development as well as basic to advanced research about the organism and the infection. The review focuses on the life cycle, targets, and possible therapeutic strategies, which can lead to further research and development of COVID-19 therapy.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals Optimal Renovation Strategies through Life-Cycle Analysis in a Pilot Building Located in a Mild Mediterranean Climate

2021 ◽  
Vol 11 (4) ◽  
pp. 1423
Author(s):  
José Manuel Salmerón Lissen ◽  
Cristina Isabel Jareño Escudero ◽  
Francisco José Sánchez de la Flor ◽  
Miriam Navarro Escudero ◽  
Theoni Karlessi ◽  
...  
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Mediterranean Climate ◽  
Building Envelope ◽  
Economic Life ◽  
Hot Water ◽  
Optimal Solutions ◽  
Building Stock ◽  
Existing Building ◽  
The Cost

The 2030 climate and energy framework includes EU-wide targets and policy objectives for the period 2021–2030 of (1) at least 55% cuts in greenhouse gas emissions (from 1990 levels); (2) at least 32% share for renewable energy; and (3) at least 32.5% improvement in energy efficiency. In this context, the methodology of the cost-optimal level from the life-cycle cost approach has been applied to calculate the cost of renovating the existing building stock in Europe. The aim of this research is to analyze a pilot building using the cost-optimal methodology to determine the renovation measures that lead to the lowest life-cycle cost during the estimated economic life of the building. The case under study is an apartment building located in a mild Mediterranean climate (Castellon, SP). A package of 12 optimal solutions has been obtained to show the importance of the choice of the elements and systems for renovating building envelopes and how energy and economic aspects influence this choice. Simulations have shown that these packages of optimal solutions (different configurations for the building envelope, thermal bridges, airtightness and ventilation, and domestic hot water production systems) can provide savings in the primary energy consumption of up to 60%.

scholarly journals Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1792
Author(s):  
Juan Hagad ◽  
Tsukasa Kimura ◽  
Ken-ichi Fukui ◽  
Masayuki Numao
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Emotion Classification ◽  
Limited Data ◽  
Neural Network Architecture ◽  
Building Models ◽  
The Subject ◽  
Data Constraints ◽  
Input Level ◽  
Normally Distributed

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. Variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

scholarly journals Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Detection Efficiency ◽  
Evaluation Model ◽  
Smart Meter ◽  
Smart Meters ◽  
Abnormal Detection ◽  
Prediction Residual ◽  
Threshold Setting ◽  
Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

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