scholarly journals Intelligent Identification of Coal Structure for the Control of Heat-Induced Gas Outburst and Energy-Efficient Mining

2020 ◽  
Vol 38 (4) ◽  
pp. 839-846
Author(s):  
Tao Li ◽  
Banghua Yao ◽  
Zhihui Wen ◽  
Dengke Wang ◽  
Hongtu Zhang
Keyword(s):  
Energy Efficient ◽  
Structure Type ◽  
Identification Algorithm ◽  
Effective Prevention ◽  
Coal Structure ◽  
Gas Outburst ◽  
Proposed Model ◽  
Different Types ◽  
Tectonic Coal ◽  
Safe Mining

Coal structure could be roughly divided into four types. Among them, the two kinds of tectonic coal face a high risk of heat-induced gas outburst, which arises from the unfavorable temperature conditions in the coal structure. However, there is not yet an efficient way to identify the type of coal structure. The adjacent types of coal structures are often misjudged. The lack of an efficient identification method hinders the prevention of heat-induced gas outburst, making it difficult to realize energy-efficient and safe mining. To solve the problem, this paper first theoretically analyzes the ultrasonic properties of different types of coals, and applies backpropagation neural network (BPNN) to build up an intelligent identification model for the type of coal structure. Specifically, the characteristic parameters of ultrasonic signal were taken as the basis for judging the type of coal structure, the identification algorithm of BPNN was adopted to accurately identify the structure type of coal, and then the heat-induced gas outburst risk of the coal was evaluated preliminarily. Experimental results show that the proposed model could accurately identify the type of coal structure, and even differentiate between adjacent types of coals. The research results provide a reference for effective prevention of heat-induced gas outburst, and realization of energy-efficient and safe mining.

scholarly journals Application of Blockchain in Education: GDPR-Compliant and Scalable Certification and Verification of Academic Information

2021 ◽  
Vol 11 (10) ◽  
pp. 4537
Author(s):  
Christian Delgado-von-Eitzen ◽  
Luis Anido-Rifón ◽  
Manuel J. Fernández-Iglesias
Keyword(s):  
Data Protection ◽  
Personal Data ◽  
General Data Protection Regulation ◽  
Certification System ◽  
Proposed Model ◽  
Different Types ◽  
International Regulations ◽  
Innovative Solution ◽  
General Data ◽  
Academic Information

Blockchain technologies are awakening in recent years the interest of different actors in various sectors and, among them, the education field, which is studying the application of these technologies to improve information traceability, accountability, and integrity, while guaranteeing its privacy, transparency, robustness, trustworthiness, and authenticity. Different interesting proposals and projects were launched and are currently being developed. Nevertheless, there are still issues not adequately addressed, such as scalability, privacy, and compliance with international regulations such as the General Data Protection Regulation in Europe. This paper analyzes the application of blockchain technologies and related challenges to issue and verify educational data and proposes an innovative solution to tackle them. The proposed model supports the issuance, storage, and verification of different types of academic information, both formal and informal, and complies with applicable regulations, protecting the privacy of users’ personal data. This proposal also addresses the scalability challenges and paves the way for a global academic certification system.

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

scholarly journals Design and Analysis of High-Efficient Driver Model for LED Luminaires

2021 ◽  
pp. 96-106
Author(s):  
Onur Akalp ◽  
Harun Ozbay ◽  
Serhat Berat Efe
Keyword(s):  
Energy Efficient ◽  
Supply System ◽  
Operating Conditions ◽  
Driver Model ◽  
Zero Voltage Switching ◽  
Proposed Model ◽  
High Efficient ◽  
Driver Circuit ◽  
Low Volume ◽  
Zero Voltage

LED luminaires need a driver circuit for working properly. Most of the drivers have disadvantages such as losses during operation. This issue becomes more important while supplying with limited sources such as renewables. To overcome the problem, this study proposes a novel energy efficient driver for LED luminaires based on zero voltage switching (ZVS) single-ended primary inductance converter (SEPIC) technology. Driver and hence luminaires were designed to be fed from photovoltaic (PV) panels. In addition, an adaptive MPPT algorithm was developed to obtain optimum efficiency from supply system. SEPIC approach was preferred for MPPT application due to its advantages such as non-reversing polarity. This feature allows energy efficiency in corporation with ZVS. Proposed model was designed under PSIM platform with all components; PV panels, ZVS, SEPIC, and LED luminaires. A detailed analysis was performed by using system graphs under various operating conditions as different irradiance levels. Results show that proposed model is energy efficient and modular because of its low-volume structure. Therefore the model can lead smaller driver circuits with minimum losses.

scholarly journals Guiding Effect of Soft Tectonic Coal on Coal and Gas Outburst and its Experimental Study

2021 ◽  
Author(s):  
Qingyi Tu ◽  
Sheng Xue ◽  
Yuanping Cheng ◽  
Wei Zhang ◽  
Gaofeng Shi ◽  
...  
Keyword(s):  
Physical Simulation ◽  
Local Existence ◽  
Coal Pillar ◽  
Gas Pressure ◽  
Coal And Gas Outburst ◽  
Control Group ◽  
Long Distance ◽  
Gas Outburst ◽  
Soft Coal ◽  
Tectonic Coal

Abstract Soft tectonic coal commonly exists in coal and gas outburst zones. The physical simulation experiment was carried out to reproduce the influences of soft coal area on the outburst, and the guiding action mechanism of soft tectonic coal on the outburst was investigated. This study concludes that the amount of outburst coal in the experiments of group with local existence of soft coal area are relatively lower. The outburst coal amount (3.8035 kg) and relative outburst intensity (21.02%) in the GR5# experiment were both lower than that in the GN6# experiment of control group. However, the outburst coal in the experiments of group with local existence of soft coal area could be commonly migrated to a long distance, the maximum throwing distances in the three experiments were all over 16.73 m, reaching as high as 20.10 m. Under the gas pressure of 0.30 MPa in the group with local existence of soft coal area, the outburst coal amount (2.7355 kg) was smaller than the amount (2.803 kg) of pulverized coal filled, and the 2.0 cm coal pillar experiences failure only nearby the outburst mouth. As the gas pressure increases, the failure degree of the coal pillar becomes higher and higher until complete failure. The outburst development sequence is changed due to the existence of the soft tectonic soft area. Once the sealing conditions are destructed, the outburst firstly develops in the soft tectonic coal area. Nevertheless, sufficient energy is supplied to transport the coal mass in the soft tectonic coal area to a farther distance, while the residual outburst energy can just result in the outburst of a small quantity of coal masses in the normal area. This research will be of great scientific significance for explaining the soft tectonic coal-induced change of outburst starting and development sequence.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

scholarly journals Passivo e Balanço Socioambiental: desenvolvimento de um modelo de diagnóstico de sustentabilidade10.5773/rgsa.v3i3.175

2009 ◽  
Vol 3 (3) ◽  
pp. 38
Author(s):  
Takeshy Tachizawa ◽  
Hamilton Pozo
Keyword(s):  
Grounded Theory ◽  
Environmental Cost ◽  
Environmental Costs ◽  
Environmental Mapping ◽  
Proposed Model ◽  
The Social ◽  
Data Architecture ◽  
Theoretical View ◽  
Different Types ◽  
Environmental Development

Este artigo apresenta uma proposta de arquitetura de dados de sustentabilidade para subsidiar o monitoramento de custos socioambientais nas empresas. Apóia-se em uma base de dados de indicadores de desenvolvimento socioambiental, concebida como resultado de pesquisa empírica, desenvolvida pelo método da grounded theory. A ênfase da grounded theory é o aprendizado a partir dos dados (interativa e indutiva), e não a partir de uma visão teórica existente (dedutiva). Tais indicadores, além de refletirem o estágio de sustentabilidade em que se encontraria uma determinada empresa, subsidiariam o mapeamento socioambiental dos diferentes segmentos econômicos do universo empresarial brasileiro. O modelo proposto, não-prescritivo, sugere que na gestão de custos socioambientais, sejam adotados enfoques distintos de sustentabilidade para diferentes tipos de organizações que, em razão de seu ramo de negócios, sofrem efeitos diferenciados. Palavras-chave: Desenvolvimento sustentável; Custo Socioambiental; Sustentabilidade. Abstract This paper presents data architecture to support sustainability of the monitoring of environmental costs in companies. It is based on a database of indicators of social and environmental development, conceived as a result of empirical research, developed by the method of grounded theory. The emphasis of grounded theory is the learning from the data (interactive and inductive), and not from an existing theoretical view (deductive). Such indicators also reflect the stage of sustainability in which a given company is and would support the social and environmental mapping of the different segments of the Brazilian business. The proposed model, non-prescriptive, suggests that the management of social and environmental costs adopt distinct approaches to sustainability for different types of organizations, because their business suffers different effects. Keywords: Sustainable Development; Social and Environmental Cost; Sustainability.

Research on the Strategy of Multi Sensor Information Fusion in Coal Mine Disaster Scene

2012 ◽  
Vol 220-223 ◽  
pp. 1929-1933
Author(s):  
Yu Qing Wang ◽  
Yu Xin Qin ◽  
Zhi Guo Li ◽  
Le Deng
Keyword(s):  
Information Fusion ◽  
Coal Mine ◽  
Coal Mines ◽  
Outburst Flood ◽  
Roof Collapse ◽  
Gas Outburst ◽  
Different Types ◽  
Sensor Information

Different types of disasters occur frequently in coal mines. This paper analyzed the characteristics of different disasters, chosen the corresponding sensors to collect the information of disaster scene, and discussed the methods of multi sensor information fusion. Lastly, the multi-sensor information fusion strategies for fire, gas outburst, flood, and roof collapse were proposed in this research.

A multi-objective optimization model for logistic planning in the crisis response phase

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Mahdi Ershadi ◽  
Hossein Shams Shemirani
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Crisis Response ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Different Types ◽  
Response Phase ◽  
Logistic Planning

PurposeProper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.Design/methodology/approachThe main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.FindingsThe performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.Practical implicationsThe proposed methodology can be applied to find the best response plan for all crises.Originality/valueIn this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

scholarly journals Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network

2020 ◽  
Vol 10 (18) ◽  
pp. 6359 ◽  
Author(s):  
Shuangjie Liu ◽  
Jiaqi Xie ◽  
Changqing Shen ◽  
Xiaofeng Shang ◽  
Dong Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Vibration Signal ◽  
Deep Belief Network ◽  
Mechanical Equipment ◽  
Belief Network ◽  
Bearing Faults ◽  
Qualitative And Quantitative ◽  
Proposed Model ◽  
Different Types

Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.

scholarly journals Comparative Analysis of an Off-grid PV System for Different Types of Batteries

2018 ◽  
Vol 9 (1) ◽  
pp. 11-19
Author(s):  
Rebeka Raff ◽  
Velimir Golub ◽  
Jurica Perko
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Life Time ◽  
Optimal Size ◽  
Pv System ◽  
Eligibility Criteria ◽  
Proposed Model ◽  
Different Types ◽  
Daily Load ◽  
Lead Acid

The aim of this paper is to find an optimal size of different components of an off-grid PV system in the HOMER software with different types of batteries (lead-acid batteries and lithium-ion batteries). The proposed model shows the optimal size of the off-grid PV system for a holiday cottage with regard to eligibility criteria for various types of batteries and the net present cost (NPC). The observed off-grid PV system consists of PV modules, a load, a converter and batteries and it is modelled in the HOMER software. The load is modelled with a daily load diagram for the holiday cottage. For lead-acid and lithium-ion batteries the optimal size of different components of an off-grid PV system for five different scenarios (in respect of the price and life-time) is obtained. In addition, the optimal size of the presented model with respect to different values of capacity shortage ranging from 0% to 5% is presented

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