Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempt...Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.展开更多
The disperse phase (water) droplet size of Emulsified Heavy-Oil (EHO) has a significant effect on the Combustion of Water-in-Heavy oil Emulsion. In this paper, the disperse degree of disperse phase in EHO is measured ...The disperse phase (water) droplet size of Emulsified Heavy-Oil (EHO) has a significant effect on the Combustion of Water-in-Heavy oil Emulsion. In this paper, the disperse degree of disperse phase in EHO is measured in various experimental conditions, and the relationship of the disperse degree with the agitating degree is investigated. The following results are obtained: with the increases of agitating speed, agitating time and agitator diameter, disperse degree become better. The experimental results show that the disperse degree becomes much better when n is 1*#200r/min and D m is 55mm. The D SV of disperse phase in EHO will decrease as the increasing of agitating speed and agitator diameter, which is consistent with the energy theory. Then according to dimensional analysis, an experimental formula to calculate the surface area of water particle per unit volume is given. 5 figs. 4tabs. 9refs.展开更多
The stability of emulsified heavy oil(EHO) has a significant effect on the combustion of water in heavy oil emulsion Based on the prior experimental studies, the influential factors are studied systematically before/a...The stability of emulsified heavy oil(EHO) has a significant effect on the combustion of water in heavy oil emulsion Based on the prior experimental studies, the influential factors are studied systematically before/after magnetization in various experimental conditions It is found that the stability of EHO is related to fuel type, the percentage of water added in oil(PWA) , Emulsifier added in oil(PEA),magnetic strength and the tube material The following results are obtained: the stability of EHO decreases as the increase of PWA and the decrease of PEA before/after magnetization; in the same experimental conditions, the stability of EHO becomes better after magnetization, when PWA varies in the range from 7 5% to 12 5% and, PEA being bigger than 0 03%, there is a better stability; No 100,No 200 and CL 1 oil has the different stability, the order is No 100>No 200>CL 1; the higher the magnetic strength, the better the stability of EHO; in view of energy consumption and the stability, the rational value of magnetic strength is from 1?200?GS to 1?400?GS; using stainless steel tube has a better effect on stability than using iron展开更多
Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and...Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.展开更多
Renewable fuels like hydrogen and biodiesels can very well suit to diesel engine applications as they address problems of energy scarcity, foreign exchange savings and emission norms. Production of hydrogen and biodie...Renewable fuels like hydrogen and biodiesels can very well suit to diesel engine applications as they address problems of energy scarcity, foreign exchange savings and emission norms. Production of hydrogen and biodiesel to industrial scale with low cost techniques can pave way for their efficient use in engine applications. In view of this, an attempt has been made to operate a modified diesel engine on these high potential renewable fuel combinations. An experimental study was carried out to evaluate the performance, combustion and emission characteristics of diesel engine operated in dual fuel (DF) mode fuelled with esters of honne (EHNO), honge (EHO) oils and hydrogen induction. The study revealed that the brake thermal efficiency increased up to 20% hydrogen energy ratio (HER) and then it decreased. The emissions such as hydrocarbon (HC), Carbon monoxide (CO) and smoke decreased with HER while oxides of nitrogen (NOx) increased. The combustion parameters like peak pressure, ignition delay and heat release rate (HRR) increased with HER.展开更多
<span style="font-family:Verdana;">This methodological paper explains the process of occupational therapy management of displaced persons due to disaster influence. It is yet to be part of the occupati...<span style="font-family:Verdana;">This methodological paper explains the process of occupational therapy management of displaced persons due to disaster influence. It is yet to be part of the occupational therapy practice domain even in recent decades with the advancement in occupational therapy research evidence and development. It is based on the current knowledge and developments in occupational therapy client centered practice. This methodological and conceptual based study describes and illustrates the occupational therapy practice model of rehabilitating displaced persons due to disaster influence. The present paper explores the occupational therapy role in rehabilitating displaced persons through People’s Environmental Occupation, Ecology of Human Performance (EHP), and Occupational Therapy Practice Framework. These are integrated as a single conceptual model to describe how and process of occupational therapy practice area during the rehabilitation process of a displaced person(s). The paper finally presented a sequenced framework to guide occupational therapists practice in this new emerging practice area of rehabilitation. This study was developed based on three different theories to support the occupational therapy treatment process of a displaced person due to disaster of any nature. Its concept based without any statistical analysis.</span>展开更多
为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模...为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模型,将定位问题转化为寻优求解问题。然后,利用改进象群优化算法和OPTICS(ordering points to identify the clustering structure)聚类算法对第1次寻优结果中多端行波信息差异矩阵的各元素进行聚类分析,找出存在时间同步误差的坏数据。最后,在考虑正常数据的情况下进行第2次寻优,实现故障的精确定位。仿真结果表明,该方法在不需预设行波波速的情况下,能够实现较准确的故障定位,且具有较强时间误差鲁棒性。展开更多
The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with th...The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with the help of a machine learning algorithm i.e.Elephant Herd Optimization(EHO).Three different types of time series data are used to testify the superiority of the proposed method namely stock market data,currency exchange data and absenteeism at work.The data are first subjected to feature selection methods namely ANOVA and Friedman test.The feature selection methods provide relevant set of features which is fed to the neural network trained with the method.The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization.The results prove supremacy of EHO over other methods.展开更多
Purpose-The principal intention behind the activity is to regulate the speed,current and commutation of the brushless DC(BLDC)motor.Thereby,the authors can control the torque.Design/methodology/approach-In order to re...Purpose-The principal intention behind the activity is to regulate the speed,current and commutation of the brushless DC(BLDC)motor.Thereby,the authors can control the torque.Design/methodology/approach-In order to regulate the current and speed of the motor,the Multiresolution PID(MRPID)controller is proposed.The altered Landsman converter is utilized in this proposed suppression circuit,and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor.The adaptive neuro-fuzzy inference system-elephant herding optimization(ANFISEHO)calculation mirrors the conduct of the procreant framework in families.Findings-Brushless DC motor’s dynamic properties are created,noticed and examined by MATLAB/Simulink model.The performance will be compared with existing genetic algorithms.Originality/value-The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.展开更多
文摘Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.
文摘The disperse phase (water) droplet size of Emulsified Heavy-Oil (EHO) has a significant effect on the Combustion of Water-in-Heavy oil Emulsion. In this paper, the disperse degree of disperse phase in EHO is measured in various experimental conditions, and the relationship of the disperse degree with the agitating degree is investigated. The following results are obtained: with the increases of agitating speed, agitating time and agitator diameter, disperse degree become better. The experimental results show that the disperse degree becomes much better when n is 1*#200r/min and D m is 55mm. The D SV of disperse phase in EHO will decrease as the increasing of agitating speed and agitator diameter, which is consistent with the energy theory. Then according to dimensional analysis, an experimental formula to calculate the surface area of water particle per unit volume is given. 5 figs. 4tabs. 9refs.
文摘The stability of emulsified heavy oil(EHO) has a significant effect on the combustion of water in heavy oil emulsion Based on the prior experimental studies, the influential factors are studied systematically before/after magnetization in various experimental conditions It is found that the stability of EHO is related to fuel type, the percentage of water added in oil(PWA) , Emulsifier added in oil(PEA),magnetic strength and the tube material The following results are obtained: the stability of EHO decreases as the increase of PWA and the decrease of PEA before/after magnetization; in the same experimental conditions, the stability of EHO becomes better after magnetization, when PWA varies in the range from 7 5% to 12 5% and, PEA being bigger than 0 03%, there is a better stability; No 100,No 200 and CL 1 oil has the different stability, the order is No 100>No 200>CL 1; the higher the magnetic strength, the better the stability of EHO; in view of energy consumption and the stability, the rational value of magnetic strength is from 1?200?GS to 1?400?GS; using stainless steel tube has a better effect on stability than using iron
基金The authors would like to acknowledge the support of Taif UniversityResearchers Supporting Project number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.
文摘Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.
文摘Renewable fuels like hydrogen and biodiesels can very well suit to diesel engine applications as they address problems of energy scarcity, foreign exchange savings and emission norms. Production of hydrogen and biodiesel to industrial scale with low cost techniques can pave way for their efficient use in engine applications. In view of this, an attempt has been made to operate a modified diesel engine on these high potential renewable fuel combinations. An experimental study was carried out to evaluate the performance, combustion and emission characteristics of diesel engine operated in dual fuel (DF) mode fuelled with esters of honne (EHNO), honge (EHO) oils and hydrogen induction. The study revealed that the brake thermal efficiency increased up to 20% hydrogen energy ratio (HER) and then it decreased. The emissions such as hydrocarbon (HC), Carbon monoxide (CO) and smoke decreased with HER while oxides of nitrogen (NOx) increased. The combustion parameters like peak pressure, ignition delay and heat release rate (HRR) increased with HER.
文摘<span style="font-family:Verdana;">This methodological paper explains the process of occupational therapy management of displaced persons due to disaster influence. It is yet to be part of the occupational therapy practice domain even in recent decades with the advancement in occupational therapy research evidence and development. It is based on the current knowledge and developments in occupational therapy client centered practice. This methodological and conceptual based study describes and illustrates the occupational therapy practice model of rehabilitating displaced persons due to disaster influence. The present paper explores the occupational therapy role in rehabilitating displaced persons through People’s Environmental Occupation, Ecology of Human Performance (EHP), and Occupational Therapy Practice Framework. These are integrated as a single conceptual model to describe how and process of occupational therapy practice area during the rehabilitation process of a displaced person(s). The paper finally presented a sequenced framework to guide occupational therapists practice in this new emerging practice area of rehabilitation. This study was developed based on three different theories to support the occupational therapy treatment process of a displaced person due to disaster of any nature. Its concept based without any statistical analysis.</span>
文摘为提高含电缆-架空混合线路配电网故障定位精度,提出一种基于改进象群优化算法的配电网混合线路故障定位方法。首先,根据故障行波的传播特性以及配电网的结构特点,分析了多端行波信息差异矩阵的“唯一性”,并以此为基础构建了故障定位模型,将定位问题转化为寻优求解问题。然后,利用改进象群优化算法和OPTICS(ordering points to identify the clustering structure)聚类算法对第1次寻优结果中多端行波信息差异矩阵的各元素进行聚类分析,找出存在时间同步误差的坏数据。最后,在考虑正常数据的情况下进行第2次寻优,实现故障的精确定位。仿真结果表明,该方法在不需预设行波波速的情况下,能够实现较准确的故障定位,且具有较强时间误差鲁棒性。
文摘The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with the help of a machine learning algorithm i.e.Elephant Herd Optimization(EHO).Three different types of time series data are used to testify the superiority of the proposed method namely stock market data,currency exchange data and absenteeism at work.The data are first subjected to feature selection methods namely ANOVA and Friedman test.The feature selection methods provide relevant set of features which is fed to the neural network trained with the method.The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization.The results prove supremacy of EHO over other methods.
文摘Purpose-The principal intention behind the activity is to regulate the speed,current and commutation of the brushless DC(BLDC)motor.Thereby,the authors can control the torque.Design/methodology/approach-In order to regulate the current and speed of the motor,the Multiresolution PID(MRPID)controller is proposed.The altered Landsman converter is utilized in this proposed suppression circuit,and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor.The adaptive neuro-fuzzy inference system-elephant herding optimization(ANFISEHO)calculation mirrors the conduct of the procreant framework in families.Findings-Brushless DC motor’s dynamic properties are created,noticed and examined by MATLAB/Simulink model.The performance will be compared with existing genetic algorithms.Originality/value-The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.
