Vibration spectrum imaging : A novel bearing fault classification approach
- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell
- Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Electronics Vol. 62, no. 1 (2015), p. 494-502
- Full Text: false
- Reviewed:
- Description: Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.
An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms
- Linardon, Jake, Fuller-Tyszkiewicz, Matthew, Shatte, Adrian, Greenwood, Christopher
- Authors: Linardon, Jake , Fuller-Tyszkiewicz, Matthew , Shatte, Adrian , Greenwood, Christopher
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Eating Disorders Vol. 55, no. 6 (2022), p. 845-850
- Full Text:
- Reviewed:
- Description: Objective: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms. Method: Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM). Results: The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48–0.52), but adequate for symptom-level change (R2 =.15–.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75–0.93) and adherence (AUC = 0.92–0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors. Conclusion: A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made. © 2022 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC.
- Authors: Linardon, Jake , Fuller-Tyszkiewicz, Matthew , Shatte, Adrian , Greenwood, Christopher
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Eating Disorders Vol. 55, no. 6 (2022), p. 845-850
- Full Text:
- Reviewed:
- Description: Objective: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms. Method: Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM). Results: The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48–0.52), but adequate for symptom-level change (R2 =.15–.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75–0.93) and adherence (AUC = 0.92–0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors. Conclusion: A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made. © 2022 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC.
Belief in conspiracy theories : the predictive role of schizotypy, machiavellianism, and primary psychopathy
- Authors: March, Evita , Springer
- Date: 2019
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 14, no. 12 (2019), p.
- Full Text:
- Reviewed:
- Description: A conspiracy theory refers to an alternative explanation of an event involving a conspirator plot organised by powerful people or organisations. Belief in conspiracy theories is related to negative societal outcomes such as poor medical decisions and a decrease in prosocial behaviour. Given these negative outcomes, researchers have explored predictors of belief in conspiracy theories in an attempt to understand and possibly manage these beliefs. In the current study, we explored the utility of personality in predicting belief in conspiracy theories. The aim of the current study was to explore the utility of the odd beliefs/magical thinking subtype of schizotypy, Machiavellianism, grandiose narcissism, vulnerable narcissism, primary psychopathy, and secondary psychopathy in predicting belief in conspiracy theories. Participants (N = 230; 44.7% male, 55.3% female) completed an anonymous, confidential online questionnaire which comprised demographics and measures of personality traits and belief in conspiracy theories. The total regression model indicated odd beliefs/magical thinking, trait Machiavellianism, and primary psychopathy were significant, positive predictors of belief in conspiracy theories. No other predictors reached significance. Results of the current study highlight individuals who might be more susceptible to believing conspiracy theories. Specifically, these results indicate that the individual more likely to believe in conspiracy theories may have unusual patterns of thinking and cognitions, be strategic and manipulative, and display interpersonal and affective deficits. © 2019 March, Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Authors: March, Evita , Springer
- Date: 2019
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 14, no. 12 (2019), p.
- Full Text:
- Reviewed:
- Description: A conspiracy theory refers to an alternative explanation of an event involving a conspirator plot organised by powerful people or organisations. Belief in conspiracy theories is related to negative societal outcomes such as poor medical decisions and a decrease in prosocial behaviour. Given these negative outcomes, researchers have explored predictors of belief in conspiracy theories in an attempt to understand and possibly manage these beliefs. In the current study, we explored the utility of personality in predicting belief in conspiracy theories. The aim of the current study was to explore the utility of the odd beliefs/magical thinking subtype of schizotypy, Machiavellianism, grandiose narcissism, vulnerable narcissism, primary psychopathy, and secondary psychopathy in predicting belief in conspiracy theories. Participants (N = 230; 44.7% male, 55.3% female) completed an anonymous, confidential online questionnaire which comprised demographics and measures of personality traits and belief in conspiracy theories. The total regression model indicated odd beliefs/magical thinking, trait Machiavellianism, and primary psychopathy were significant, positive predictors of belief in conspiracy theories. No other predictors reached significance. Results of the current study highlight individuals who might be more susceptible to believing conspiracy theories. Specifically, these results indicate that the individual more likely to believe in conspiracy theories may have unusual patterns of thinking and cognitions, be strategic and manipulative, and display interpersonal and affective deficits. © 2019 March, Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Green demand aware fog computing : a prediction-based dynamic resource provisioning approach
- Khadhijah, Pg, Newaz, S., Rahman, Fatin, Lee, Gyu, Karmakar, Gour, Au, Thien-Wan
- Authors: Khadhijah, Pg , Newaz, S. , Rahman, Fatin , Lee, Gyu , Karmakar, Gour , Au, Thien-Wan
- Date: 2022
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 11, no. 4 (2022), p.
- Full Text:
- Reviewed:
- Description: Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Khadhijah, Pg , Newaz, S. , Rahman, Fatin , Lee, Gyu , Karmakar, Gour , Au, Thien-Wan
- Date: 2022
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 11, no. 4 (2022), p.
- Full Text:
- Reviewed:
- Description: Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
An application of high-dimensional statistics to predictive modeling of grade variability
- Hinz, Juri, Grigoryev, Igor, Novikov, Alexander
- Authors: Hinz, Juri , Grigoryev, Igor , Novikov, Alexander
- Date: 2020
- Type: Text , Journal article
- Relation: Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Hinz, Juri , Grigoryev, Igor , Novikov, Alexander
- Date: 2020
- Type: Text , Journal article
- Relation: Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Agoraphilic navigation algorithm in dynamic environment with and without prediction of moving objects location
- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan, Choudhury, Tanveer
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
- Full Text:
- Reviewed:
- Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
- Description: E1
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
- Full Text:
- Reviewed:
- Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
- Description: E1
Undiagnosed cryptic diversity in small, microendemic frogs (Leptolalax) from the Central Highlands of Vietnam
- Rowley, Jodi, Tran, Dao, Frankham, Greta, Dekker, Anthony, Le, Duong, Nguyen, Truong, Dau, Vinh, Hoang, Huy
- Authors: Rowley, Jodi , Tran, Dao , Frankham, Greta , Dekker, Anthony , Le, Duong , Nguyen, Truong , Dau, Vinh , Hoang, Huy
- Date: 2015
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 10, no. 5 (2015), p. 1-21
- Full Text:
- Reviewed:
- Description: A major obstacle in prioritizing species or habitats for conservation is the degree of unrecognized diversity hidden within complexes of morphologically similar, "cryptic" species. Given that amphibians are one of the most threatened groups of organisms on the planet, our inability to diagnose their true diversity is likely to have significant conservation consequences. This is particularly true in areas undergoing rapid deforestation, such as Southeast Asia. The Southeast Asian genus Leptolalax is a group of small-bodied, morphologically conserved frogs that inhabit the forest-floor. We examined a particularly smallbodied and morphologically conserved subset, the Leptolalax applebyi group, using a combination of molecular, morphometric, and acoustic data to identify previously unknown diversity within. In order to predict the geographic distribution of the group, estimate the effects of habitat loss and assess the degree of habitat protection, we used our locality data to perform ecological niche modelling using MaxEnt. Molecular (mtDNA and nuDNA), acoustic and subtle morphometric differences revealed a significant underestimation of diversity in the L. applebyi group; at least two-thirds of the diversity may be unrecognised. Patterns of diversification and microendemism in the group appear driven by limited dispersal, likely due to their small body size, with several lineages restricted to watershed basins. The L. applebyi group is predicted to have historically occurred over a large area of the Central Highlands of Vietnam, a considerable portion of which has already been deforested. Less than a quarter of the remaining forest predicted to be suitable for the group falls within current protected areas. The predicted distribution of the L. applebyi group extends into unsurveyed watershed basins, each potentially containing unsampled diversity, some of which may have already been lost due to deforestation. Current estimates of amphibian diversity based on morphology alone are misleading, and accurate alpha taxonomy is essential to accurately prioritize conservation efforts. © 2015 Rowley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Authors: Rowley, Jodi , Tran, Dao , Frankham, Greta , Dekker, Anthony , Le, Duong , Nguyen, Truong , Dau, Vinh , Hoang, Huy
- Date: 2015
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 10, no. 5 (2015), p. 1-21
- Full Text:
- Reviewed:
- Description: A major obstacle in prioritizing species or habitats for conservation is the degree of unrecognized diversity hidden within complexes of morphologically similar, "cryptic" species. Given that amphibians are one of the most threatened groups of organisms on the planet, our inability to diagnose their true diversity is likely to have significant conservation consequences. This is particularly true in areas undergoing rapid deforestation, such as Southeast Asia. The Southeast Asian genus Leptolalax is a group of small-bodied, morphologically conserved frogs that inhabit the forest-floor. We examined a particularly smallbodied and morphologically conserved subset, the Leptolalax applebyi group, using a combination of molecular, morphometric, and acoustic data to identify previously unknown diversity within. In order to predict the geographic distribution of the group, estimate the effects of habitat loss and assess the degree of habitat protection, we used our locality data to perform ecological niche modelling using MaxEnt. Molecular (mtDNA and nuDNA), acoustic and subtle morphometric differences revealed a significant underestimation of diversity in the L. applebyi group; at least two-thirds of the diversity may be unrecognised. Patterns of diversification and microendemism in the group appear driven by limited dispersal, likely due to their small body size, with several lineages restricted to watershed basins. The L. applebyi group is predicted to have historically occurred over a large area of the Central Highlands of Vietnam, a considerable portion of which has already been deforested. Less than a quarter of the remaining forest predicted to be suitable for the group falls within current protected areas. The predicted distribution of the L. applebyi group extends into unsurveyed watershed basins, each potentially containing unsampled diversity, some of which may have already been lost due to deforestation. Current estimates of amphibian diversity based on morphology alone are misleading, and accurate alpha taxonomy is essential to accurately prioritize conservation efforts. © 2015 Rowley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Functional data modelling approach for analysing and predicting trends in incidence rates-an application to falls injury
- Ullah, Shahid, Finch, Caroline
- Authors: Ullah, Shahid , Finch, Caroline
- Date: 2010
- Type: Text , Journal article
- Relation: Osteoporosis International Vol. 21, no. 12 (2010), p. 2125-2134
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Summary: Policy decisions about the allocation of current and future resources should be based on the most accurate predictions possible. A functional data analysis (FDA) approach improves the understanding of current trends and future incidence of injuries. FDA provides more valid and reliable long-term predictions than commonly used methods. Introduction: Accurate information about predicted future injury rates is needed to inform public health investment decisions. It is critical that such predictions derived from the best available statistical models to minimise possible error in future injury incidence rates. Methods: FDA approach was developed to improve long-term predictions but is yet to be widely applied to injury epidemiology or other epidemiological research. Using the specific example of modelling age-specific annual incidence of fall-related severe head injuries of older people during 1970-2004 and predicting rates up to 2024 in Finland, this paper explains the principles behind FDA and demonstrates their superiority in terms of prediction accuracy over the more commonly reported ordinary least squares (OLS) approach. Results: Application of the FDA approach shows that the incidence of fall-related severe head injuries would increase by 2.3-2.6-fold by 2024 compared to 2004. The FDA predictions had 55% less prediction error than traditional OLS predictions when compared to actual data. Conclusions: In summary, FDA provides more accurate predictions of long-term incidence trends than commonly used methods. The production of FDA prediction intervals for future injury incidence rates gives likely guidance as to the likely accuracy of these predictions. © 2010 International Osteoporosis Foundation and National Osteoporosis Foundation.
- Authors: Ullah, Shahid , Finch, Caroline
- Date: 2010
- Type: Text , Journal article
- Relation: Osteoporosis International Vol. 21, no. 12 (2010), p. 2125-2134
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Summary: Policy decisions about the allocation of current and future resources should be based on the most accurate predictions possible. A functional data analysis (FDA) approach improves the understanding of current trends and future incidence of injuries. FDA provides more valid and reliable long-term predictions than commonly used methods. Introduction: Accurate information about predicted future injury rates is needed to inform public health investment decisions. It is critical that such predictions derived from the best available statistical models to minimise possible error in future injury incidence rates. Methods: FDA approach was developed to improve long-term predictions but is yet to be widely applied to injury epidemiology or other epidemiological research. Using the specific example of modelling age-specific annual incidence of fall-related severe head injuries of older people during 1970-2004 and predicting rates up to 2024 in Finland, this paper explains the principles behind FDA and demonstrates their superiority in terms of prediction accuracy over the more commonly reported ordinary least squares (OLS) approach. Results: Application of the FDA approach shows that the incidence of fall-related severe head injuries would increase by 2.3-2.6-fold by 2024 compared to 2004. The FDA predictions had 55% less prediction error than traditional OLS predictions when compared to actual data. Conclusions: In summary, FDA provides more accurate predictions of long-term incidence trends than commonly used methods. The production of FDA prediction intervals for future injury incidence rates gives likely guidance as to the likely accuracy of these predictions. © 2010 International Osteoporosis Foundation and National Osteoporosis Foundation.
Prediction of gold-bearing localised occurrences from limited exploration data
- Grigoryev, Igor, Bagirov, Adil, Tuck, Michael
- Authors: Grigoryev, Igor , Bagirov, Adil , Tuck, Michael
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Computational Science and Engineering Vol. 21, no. 4 (2020), p. 503-512
- Full Text: false
- Reviewed:
- Description: Inaccurate drill-core assay interpretation in the exploration stage presents challenges to long-term profit of gold mining operations. Predicting the gold distribution within a deposit as precisely as possible is one of the most important aspects of the methodologies employed to avoid problems associated with financial expectations. The prediction of the variability of gold using a very limited number of drill-core samples is a very challenging problem. This is often intractable using traditional statistical tools where with less than complete spatial information certain assumptions are made about gold distribution and mineralisation. The decision-support predictive modelling methodology based on the unsupervised machine learning technique, presented in this paper avoids some of the restrictive limitations of traditional methods. It identifies promising exploration targets missed during exploration and recovers hidden spatial and physical characteristics of the explored deposit using information directly from drill hole database. Copyright © 2020 Inderscience Enterprises Ltd.
Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations
- Zhou, Jian, Dai, Yong, Khandelwal, Manoj, Monjezi, Masoud, Yu, Zhi, Qiu, Yingui
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Full Text:
- Reviewed:
- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Full Text:
- Reviewed:
- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
- Authors: King, John
- Date: 2017
- Type: Text , Journal article
- Relation: Review of political economy Vol. 29, no. 1 (2017), p. 1-17
- Full Text: false
- Reviewed:
- Description: The extensive critical literature on Thomas Piketty's Capital in the Twenty-First Century is surveyed under nine headings. The first deals with the conservative argument that inequality in the distribution of wealth does not matter, since a rising tide lifts all boats. Second, it is claimed that Piketty's prediction of continuously increasing inequality and the return of 'patrimonial capitalism' is unjustified. Third, the quality of the empirical evidence that he cites is questioned, on a number of quite different grounds. Fourth, some critics object that Piketty's explanation of long-run trends in the distribution of wealth is too general and too theoretical. Fifth is the argument that he has used the correct (neoclassical) theory incorrectly, exaggerating the elasticity of substitution of capital for labour. Against this, post-Keynesian critics claim, sixthly, that Piketty is using the wrong theory, and should have drawn on the Kaldor-Pasinetti model of distribution and growth, and not the discredited neoclassical analysis. Seventh, Piketty has been criticised for ignoring the distribution of wealth in developing countries. Eighth, there is a wide range of objections to his most striking policy proposal, for a progressive global wealth tax. Finally, several critics from outside economics complain that Piketty has neglected a number of non-economic dimensions of inequality. I conclude by welcoming both the book and the critical literature, and calling for the distribution of wealth to be placed back on the political agenda.
Applications of soft computing methods in backbreak assessment in surface mines : a comprehensive review
- Yari, Mojtaba, Khandelwal, Manoj, Abbasi, Payam, Koutras, Evangelos, Armaghani, Danial, Asteris, Panagiotis
- Authors: Yari, Mojtaba , Khandelwal, Manoj , Abbasi, Payam , Koutras, Evangelos , Armaghani, Danial , Asteris, Panagiotis
- Date: 2024
- Type: Text , Journal article , Review
- Relation: CMES - Computer Modeling in Engineering and Sciences Vol. 140, no. 3 (2024), p. 2207-2238
- Full Text:
- Reviewed:
- Description: Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to the unintended fracturing of rock beyond the desired blast perimeter, can significantly impact project timelines and costs. This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations, specifically focusing on backbreak prediction. The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects. © 2024 Tech Science Press. All rights reserved.
- Authors: Yari, Mojtaba , Khandelwal, Manoj , Abbasi, Payam , Koutras, Evangelos , Armaghani, Danial , Asteris, Panagiotis
- Date: 2024
- Type: Text , Journal article , Review
- Relation: CMES - Computer Modeling in Engineering and Sciences Vol. 140, no. 3 (2024), p. 2207-2238
- Full Text:
- Reviewed:
- Description: Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to the unintended fracturing of rock beyond the desired blast perimeter, can significantly impact project timelines and costs. This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations, specifically focusing on backbreak prediction. The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects. © 2024 Tech Science Press. All rights reserved.
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