Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisionin...Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.展开更多
Solar photovoltaic appears to be the most interesting renewable energy in developing countries where its deposit is abundant. Unfortunately, the lack of precise knowledge of solar radiation deposit and its limited dat...Solar photovoltaic appears to be the most interesting renewable energy in developing countries where its deposit is abundant. Unfortunately, the lack of precise knowledge of solar radiation deposit and its limited data hinder optimal exploitation of solar installations. This study presents a performing model for daily global horizontal solar radiation for the five regional capitals in Togo: Lomé, Atakpamé, Sokodé, Kara and Dapaong. The data used for the study were obtained from the General Directorate of National Meteorology of Togo, for five years. The model developed combines linear and nonlinear methods with harmonic and exponential terms taking into account climatological parameters such as location latitude, daily relative humidity, daily ratio of sunshine duration and daily mean temperature. Statistical errors of the model were compared to those of two previous models elaborated for Togo and Nigeria. The results showed that the model is more efficient to predict global horizontal solar radiation over the five main cities in Togo. The comparison of estimated data and measured ones showed a good agreement between them.展开更多
Deagglomeration of cohesive particles in combination with coarse carrier is a key requirement for inhaled formulations.The aim of the project was to propose a mathematical approach to understand aerosolization behavio...Deagglomeration of cohesive particles in combination with coarse carrier is a key requirement for inhaled formulations.The aim of the project was to propose a mathematical approach to understand aerosolization behaviour of micronized particles alone and in formulation with carriers.Salbutamol sulphate and salmeterol xinafoate were blended separately with fine lactose(ratio 1:4)and fine and coarse lactose(1:4:63.5).Laser diffraction was employed to characterize the powder median particle size.The deagglomeration of micronized materials followed an asymptotic monoexponential relationship.When the coarse lactose was added,the relationship fitted a bi-exponential equation showing an easily and a poorly dispersed fraction.Using model hydrophobic and hydrophilic APIs,this study has demonstrated the utility of an analytical approach that can parameterize deagglomeration behaviour of carrier-free and carrier-based inhalation formulations.The analytical approach provides the ability to systematically study the effect of material,formulation and processing factors on deagglomeration behaviour.展开更多
In this work, activated carbons (ACs) prepared by chemical activation of garcinia cola nut shell impregnated with H3PO4 (CBH2/1) and KOH (CBK1/1) were used to study the kinetics, equilibrium and thermodynamics of the ...In this work, activated carbons (ACs) prepared by chemical activation of garcinia cola nut shell impregnated with H3PO4 (CBH2/1) and KOH (CBK1/1) were used to study the kinetics, equilibrium and thermodynamics of the adsorption of thymol blue from aqueous solution. The characterization of ACs showed the BET measurements gave surface area and total pore volume respectively of 328.407 m2·g-1 and 0.1032 cm3·g-1 for CBH2/1 and 25.962 m2·g-1 and 0.03 cm3·g-1for CBK1/1;elemental analysis showed a high percentage of carbon in both ACs. Influence of parameters such as initial pH, contact time, adsorbent mass, initial concentration, ionic strength and the effect of temperature on the removal of thymol blue from aqueous solution were studied in batch mode. The studies showed that equilibrium adsorption was attained after 60 minutes for the two ACs, adsorption capacity increased with increasing concentration of thymol blue, and maximum adsorption capacity was obtained at an acidic environment with pH 2. Avrami’s non-linear kinetic expression was the best suited for describing the adsorption kinetics of thymol blue onto ACs, while equilibrium data showed that the three-parameter isotherms better described the adsorption process since R2 > 0.96, and the error functions were lowest for all of them. Maximum adsorption capacity values obtained using the three-parameter Fritz-Schlunder equation were 32.147 mg·g-1 for CBH2/1 and 67.494 mg·g-1 for CBK1/1. The values of the model parameters g and mFS respectively, obtained using the Redlich-Peterson and Fritz-Schlunder III isotherms below 1, showed that the adsorption of thymol blue by the ACs occurred on heterogeneous surfaces. Thermodynamic analyses of the data of the adsorption of thymol blue onto ACs revealed that the adsorption process was temperature dependent, endothermic and spontaneous.展开更多
A 41-wk growth trial was conducted to evaluate the effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio) with an initial body weight of 1...A 41-wk growth trial was conducted to evaluate the effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio) with an initial body weight of 1.85 ± 0.17 g. The dietary protein levels were designed at 320(P32), 360(P36). 400(P40).and 440 g/kg(P44), respectively. The growth curves of the gibel carp for each group were fitted and analyzed with four nonlinear regression models(Gompertz. logistic. von Bertalanffy and Richards). The final body weights(mean ± SD) of the fish were 226 ± 6.231 ± 7.242 ± 2, and 236 ± 2 g for P32, P36, P40,and P44. respectively. Feed conversion ratio of P40 and P44 groups was significantly lower than that of P32 and P36 groups(P < 0.05). Productive protein value of P44 group was significantly lower than that of P32 and P36 groups, but not different from that of P40 group(P > 0.05). The growth response of the gibel carp for each group was the best fitted by Richards model with the lowest Chi^2, residual sum of squares and residual variance, then Gompertz and von Bertalanffy growth models, but the logistic model did not fit the data well justified by Chi^2 values. The optimal protein level(400 g/kg) prolonged the stage of fast growth and predicted the highest asymptotic weight, which was close to the harvest size in practice.展开更多
This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of ...This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of the application is to enhance understanding of the intricate relationships among the key process parameters.The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network(ANN).The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach,in which accuracies of the generated predictive models are often not satisfactory,and the opaqueness of the ANN models.The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system.An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO_(2) production rate are the steam flow rate through reboiler,reboiler pressure,and the CO_(2) concentration in the flue gas.展开更多
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
文摘Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.
文摘Solar photovoltaic appears to be the most interesting renewable energy in developing countries where its deposit is abundant. Unfortunately, the lack of precise knowledge of solar radiation deposit and its limited data hinder optimal exploitation of solar installations. This study presents a performing model for daily global horizontal solar radiation for the five regional capitals in Togo: Lomé, Atakpamé, Sokodé, Kara and Dapaong. The data used for the study were obtained from the General Directorate of National Meteorology of Togo, for five years. The model developed combines linear and nonlinear methods with harmonic and exponential terms taking into account climatological parameters such as location latitude, daily relative humidity, daily ratio of sunshine duration and daily mean temperature. Statistical errors of the model were compared to those of two previous models elaborated for Togo and Nigeria. The results showed that the model is more efficient to predict global horizontal solar radiation over the five main cities in Togo. The comparison of estimated data and measured ones showed a good agreement between them.
文摘Deagglomeration of cohesive particles in combination with coarse carrier is a key requirement for inhaled formulations.The aim of the project was to propose a mathematical approach to understand aerosolization behaviour of micronized particles alone and in formulation with carriers.Salbutamol sulphate and salmeterol xinafoate were blended separately with fine lactose(ratio 1:4)and fine and coarse lactose(1:4:63.5).Laser diffraction was employed to characterize the powder median particle size.The deagglomeration of micronized materials followed an asymptotic monoexponential relationship.When the coarse lactose was added,the relationship fitted a bi-exponential equation showing an easily and a poorly dispersed fraction.Using model hydrophobic and hydrophilic APIs,this study has demonstrated the utility of an analytical approach that can parameterize deagglomeration behaviour of carrier-free and carrier-based inhalation formulations.The analytical approach provides the ability to systematically study the effect of material,formulation and processing factors on deagglomeration behaviour.
文摘In this work, activated carbons (ACs) prepared by chemical activation of garcinia cola nut shell impregnated with H3PO4 (CBH2/1) and KOH (CBK1/1) were used to study the kinetics, equilibrium and thermodynamics of the adsorption of thymol blue from aqueous solution. The characterization of ACs showed the BET measurements gave surface area and total pore volume respectively of 328.407 m2·g-1 and 0.1032 cm3·g-1 for CBH2/1 and 25.962 m2·g-1 and 0.03 cm3·g-1for CBK1/1;elemental analysis showed a high percentage of carbon in both ACs. Influence of parameters such as initial pH, contact time, adsorbent mass, initial concentration, ionic strength and the effect of temperature on the removal of thymol blue from aqueous solution were studied in batch mode. The studies showed that equilibrium adsorption was attained after 60 minutes for the two ACs, adsorption capacity increased with increasing concentration of thymol blue, and maximum adsorption capacity was obtained at an acidic environment with pH 2. Avrami’s non-linear kinetic expression was the best suited for describing the adsorption kinetics of thymol blue onto ACs, while equilibrium data showed that the three-parameter isotherms better described the adsorption process since R2 > 0.96, and the error functions were lowest for all of them. Maximum adsorption capacity values obtained using the three-parameter Fritz-Schlunder equation were 32.147 mg·g-1 for CBH2/1 and 67.494 mg·g-1 for CBK1/1. The values of the model parameters g and mFS respectively, obtained using the Redlich-Peterson and Fritz-Schlunder III isotherms below 1, showed that the adsorption of thymol blue by the ACs occurred on heterogeneous surfaces. Thermodynamic analyses of the data of the adsorption of thymol blue onto ACs revealed that the adsorption process was temperature dependent, endothermic and spontaneous.
基金Financial support was provided by the Special Fund for AgroScientific Research in the Public Interest(201203015201003020)+2 种基金the National Natural Science Foundation of China Project No.3110190731372539the National Basic Research Program of China(2014CB138600)
文摘A 41-wk growth trial was conducted to evaluate the effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio) with an initial body weight of 1.85 ± 0.17 g. The dietary protein levels were designed at 320(P32), 360(P36). 400(P40).and 440 g/kg(P44), respectively. The growth curves of the gibel carp for each group were fitted and analyzed with four nonlinear regression models(Gompertz. logistic. von Bertalanffy and Richards). The final body weights(mean ± SD) of the fish were 226 ± 6.231 ± 7.242 ± 2, and 236 ± 2 g for P32, P36, P40,and P44. respectively. Feed conversion ratio of P40 and P44 groups was significantly lower than that of P32 and P36 groups(P < 0.05). Productive protein value of P44 group was significantly lower than that of P32 and P36 groups, but not different from that of P40 group(P > 0.05). The growth response of the gibel carp for each group was the best fitted by Richards model with the lowest Chi^2, residual sum of squares and residual variance, then Gompertz and von Bertalanffy growth models, but the logistic model did not fit the data well justified by Chi^2 values. The optimal protein level(400 g/kg) prolonged the stage of fast growth and predicted the highest asymptotic weight, which was close to the harvest size in practice.
基金The first author is grateful for the scholarships and generous support from the Faculty of Graduate Studies and Research,University of Regina and from the Canada Research Chair Program.
文摘This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of the application is to enhance understanding of the intricate relationships among the key process parameters.The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network(ANN).The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach,in which accuracies of the generated predictive models are often not satisfactory,and the opaqueness of the ANN models.The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system.An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO_(2) production rate are the steam flow rate through reboiler,reboiler pressure,and the CO_(2) concentration in the flue gas.
