Geothermal energy is considered a renewable,environmentally friendly,especially carbon-free,sustainable energy source that can solve the problem of climate change.In general,countries with geothermal energy resources ...Geothermal energy is considered a renewable,environmentally friendly,especially carbon-free,sustainable energy source that can solve the problem of climate change.In general,countries with geothermal energy resources are the ones going through the ring of fire.Therefore,not every country is lucky enough to own this resource.As a country with 117 active volcanoes and within the world’s ring of fire,it is a country whose geothermal resources are estimated to be about 40%of the world’s geothermal energy potential.However,the percentage used compared to the geothermal potential is too small.Therefore,this is the main energy source that Indonesia is aiming to exploit and use.However,the deployment and development of this energy source are still facing many obstacles due to many aspects from budget sources due to high capital costs,factory construction location,quality of resources,and conflicts of the local community.In this context,determining the optimal locations for geothermal energy sites(GES)is one of the most important and necessary issues.To strengthen the selection methods,this study applies a two-layer fuzzy multi-criteria decision-making method.Through the layers,the Ordinal Priority Approach(OPA)is proposed to weight the sub-criteria,the main criterion,and the sustainability factors.In layer 2,the Neutrosophic Fuzzy Axiomatic Design(NFAD)is applied to rank and evaluate potential locations for geothermal plant construction.Choosing the right geothermal energy site can bring low-cost efficiency,no greenhouse gas emissions,and quickly become the main energy source providing electricity for Indonesia.The final ranking shows Papua,Kawah Cibuni,and Moluccas as the three most suitable cities to build geothermal energy systems.Kawah Cibuni was identified as the most potential GES in Indonesia,with a score of 0.46.Papua is the second most promising GES with a score of 0.45.Next is the Moluccas,with a score of 0.39.However,the three least potential sites among the 15 studied sites are Lumut Balai,Moluccas and Patuha,with scores of 0.08,0.11 and 0.17,respectively.The conclusion of this study also classifies positions into groups to aid in decision-making.展开更多
The main aim of the paper is to present (and at the same time offer) a differ-ent perspective for the analysis of the accelerated expansion of the Universe. A perspective that can surely be considered as being “in pa...The main aim of the paper is to present (and at the same time offer) a differ-ent perspective for the analysis of the accelerated expansion of the Universe. A perspective that can surely be considered as being “in parallel” to the tradition-al ones, such as those based, for example, on the hypotheses of “Dark Matter” and “Dark Energy”, or better as a “com-possible” perspective, because it is not understood as being “exclusive”. In fact, it is an approach that, when con-firmed by experimental results, always keeps its validity from an “operative” point of view. This is because, in analogy to the traditional perspectives, on the basis of Popper’s Falsification Principle the corresponding “Generative” Logic on which it is based has not the property of the perfect induction. The basic difference then only consists in the fact that the Evolution of the Universe is now modeled by considering the Universe as a Self-Organizing System, which is thus analyzed in the light of the Maximum Ordinality Principle.展开更多
The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be mode...The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be modeled using the Maximum Ordinality Principle and its associated formal language, known as the “Incipient” Differential Calculus (IDC).展开更多
Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniq...Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.展开更多
The present paper aims at showing the possible adoption in Psychiatry of a general methodology finalized to prescribe the most appropriate Therapy based on the knowledge of its correlative effects in advance, instead ...The present paper aims at showing the possible adoption in Psychiatry of a general methodology finalized to prescribe the most appropriate Therapy based on the knowledge of its correlative effects in advance, instead of recognizing them ex post. The specific case here considered is the “bipolar disorder”, in which the adoption of three different drugs is the most common practice, although with a possible differentiation between the prescription in the morning and in the evening, respectively. Thus, the proposed methodology will consider the Ordinal Interactions between the various drugs by evaluating their combined effects, which will result as being not a simple additive “sum”, because they are evaluated on the basis of the Maximum Ordinality Principle (MOP) and, in addition, in Adherence to the Explicit Solution to the “Three-Body Problem”. In this way the Methodology here proposed is able to suggest how to account for the synergistic effects of the various drugs, especially when the latter are characterized by different concentrations and, at the same time, by generally different half-lives respectively.展开更多
This paper presents the Solution to the “Three-body Problem” in the Light of the Maximum Ordinality Principle. In the first part, however, it starts with the Solution to the Solar System, made up of “11 Bodies”. T...This paper presents the Solution to the “Three-body Problem” in the Light of the Maximum Ordinality Principle. In the first part, however, it starts with the Solution to the Solar System, made up of “11 Bodies”. This is because, in such a context, the “Three-body Problem” can be analyzed in its all descriptive possibilities. Nonetheless, the paper also presents the Solution to the “Three-body Problem” with reference to Systems totally independent from the Solar System, such as, for example, the “Triple Stars” and the “Triple Galaxies”. In this way, the paper offers a sufficiently complete framework concerning the Solution to the “Three-body Problem”, always in the Light of the Maximum Ordinality Principle, described in detail in Appendix A.展开更多
A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the ...A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making (MCGDM) and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.展开更多
The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)loss.However,when the label presents an intrinsic ordinal...The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)loss.However,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better generalization.To improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.The merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)module.First,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree.Hence,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction variance.Second,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature perturbation.During training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix.Experimental results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.展开更多
In the preparation of plasma electrolytic oxidation(PEO)coating,the rapid heating of freely-happened electron avalanche under traditional discharge(TD)mode inevitably results in a strong eruption of electric breakdown...In the preparation of plasma electrolytic oxidation(PEO)coating,the rapid heating of freely-happened electron avalanche under traditional discharge(TD)mode inevitably results in a strong eruption of electric breakdown melt.The PEO coating is loose and invariably composed of a very thin inner dense layer and an outer loose layer,as a result of which its properties and application have been limited greatly.In this work,for purpose of weakening the eruption of breakdown melt,thickening the inner dense layer,densifying the outer loose layer and improving the performance of PEO coating,ordinal discharge(OD)mode of PEO coating is developed by regulating the mass ratio of MgF_(2) to MgO(α)and voltage in the PEO investigation on AZ61 magnesium alloy in KF-KOH electrolyte.The formation mechanism under different discharge mode,electrochemical corrosion and wear of PEO coatings are investigated.The results show that the suitableαand voltage for effective OD are 1.3 and 130 V under which the freely-happened electron avalanche in MgF_(2) under TD mode can be restricted by the adequate adjacent MgO.Compared with TD mode,the inner dense layer,in which the(10¯1)plane of MgF_(2) is parallel to the(111)plane of MgO at their well-knit semi-coherent interface,is thickened to 2.4∼7.2 times,the corrosion potential(E_(corr))improvement is enlarged to 3.6∼13.2 times and the corrosion current intensity(I_(corr))is reduced from 10.8∼9.499 to 0.433(10^(−6) A/cm^(2)).The outer loose layer is densified and the wear rate is lessened 65.5%∼89.8%by the evident melioration in surface porosity,impedance and hardness.This work deepens the understanding about the discharge of PEO coating and provides an available OD mode for preparing excellent PEO coating.展开更多
The present paper aims at showing how it is possible to requalify the structures of an urban system, in order to increase its resistance and its correlative resilience, against natural calamities (earthquakes, hurrica...The present paper aims at showing how it is possible to requalify the structures of an urban system, in order to increase its resistance and its correlative resilience, against natural calamities (earthquakes, hurricanes, etc.), by adopting as reference criterion the Maximum Ordinality Principle (MOP). In this sense, the paper opens a radically new perspective in this field. In fact, the village assumed as a case study was modelled as a Self-Organizing System. This is because, although the village is usually considered as being solely made of buildings, streets, places and so on, in reality it has been conceived, planned and realized by human beings during several centuries. In addition, the people who actually leave in such an urban center, systematically deal with its maintenance, in order to possibly increase its functionality. This justifies the assumption of the village as being a Self-Organizing System and, consequently, it has been analyzed in the light of the MOP, which represents a valid reference principle for analyzing both “non-living”, “living” and “conscious” self-organizing systems.展开更多
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression probl...This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effec- tiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM sig- nificantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.展开更多
The adjacent-categories, continuation-ratio and proportional odds logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response data. We propose fitting these models with...The adjacent-categories, continuation-ratio and proportional odds logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response data. We propose fitting these models with a logarithmic link to allow estimation of different forms of the risk ratio. Each of the resulting ordinal response log-link models is a constrained version of the log multinomial model, the log-link counterpart of the multinomial logistic model. These models can be estimated using software that allows the user to specify the log likelihood as the objective function to be maximized and to impose constraints on the parameter estimates. In example data with a dichotomous covariate, the unconstrained models produced valid coefficient estimates and standard errors, and the constrained models produced plausible results. Models with a single continuous covariate performed well in data simulations, with low bias and mean squared error on average and appropriate confidence interval coverage in admissible solutions. In an application to real data, practical aspects of the fitting of the models are investigated. We conclude that it is feasible to obtain adjusted estimates of the risk ratio for ordinal outcome data.展开更多
In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy sampl...In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.展开更多
文摘Geothermal energy is considered a renewable,environmentally friendly,especially carbon-free,sustainable energy source that can solve the problem of climate change.In general,countries with geothermal energy resources are the ones going through the ring of fire.Therefore,not every country is lucky enough to own this resource.As a country with 117 active volcanoes and within the world’s ring of fire,it is a country whose geothermal resources are estimated to be about 40%of the world’s geothermal energy potential.However,the percentage used compared to the geothermal potential is too small.Therefore,this is the main energy source that Indonesia is aiming to exploit and use.However,the deployment and development of this energy source are still facing many obstacles due to many aspects from budget sources due to high capital costs,factory construction location,quality of resources,and conflicts of the local community.In this context,determining the optimal locations for geothermal energy sites(GES)is one of the most important and necessary issues.To strengthen the selection methods,this study applies a two-layer fuzzy multi-criteria decision-making method.Through the layers,the Ordinal Priority Approach(OPA)is proposed to weight the sub-criteria,the main criterion,and the sustainability factors.In layer 2,the Neutrosophic Fuzzy Axiomatic Design(NFAD)is applied to rank and evaluate potential locations for geothermal plant construction.Choosing the right geothermal energy site can bring low-cost efficiency,no greenhouse gas emissions,and quickly become the main energy source providing electricity for Indonesia.The final ranking shows Papua,Kawah Cibuni,and Moluccas as the three most suitable cities to build geothermal energy systems.Kawah Cibuni was identified as the most potential GES in Indonesia,with a score of 0.46.Papua is the second most promising GES with a score of 0.45.Next is the Moluccas,with a score of 0.39.However,the three least potential sites among the 15 studied sites are Lumut Balai,Moluccas and Patuha,with scores of 0.08,0.11 and 0.17,respectively.The conclusion of this study also classifies positions into groups to aid in decision-making.
文摘The main aim of the paper is to present (and at the same time offer) a differ-ent perspective for the analysis of the accelerated expansion of the Universe. A perspective that can surely be considered as being “in parallel” to the tradition-al ones, such as those based, for example, on the hypotheses of “Dark Matter” and “Dark Energy”, or better as a “com-possible” perspective, because it is not understood as being “exclusive”. In fact, it is an approach that, when con-firmed by experimental results, always keeps its validity from an “operative” point of view. This is because, in analogy to the traditional perspectives, on the basis of Popper’s Falsification Principle the corresponding “Generative” Logic on which it is based has not the property of the perfect induction. The basic difference then only consists in the fact that the Evolution of the Universe is now modeled by considering the Universe as a Self-Organizing System, which is thus analyzed in the light of the Maximum Ordinality Principle.
文摘The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be modeled using the Maximum Ordinality Principle and its associated formal language, known as the “Incipient” Differential Calculus (IDC).
文摘Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.
文摘The present paper aims at showing the possible adoption in Psychiatry of a general methodology finalized to prescribe the most appropriate Therapy based on the knowledge of its correlative effects in advance, instead of recognizing them ex post. The specific case here considered is the “bipolar disorder”, in which the adoption of three different drugs is the most common practice, although with a possible differentiation between the prescription in the morning and in the evening, respectively. Thus, the proposed methodology will consider the Ordinal Interactions between the various drugs by evaluating their combined effects, which will result as being not a simple additive “sum”, because they are evaluated on the basis of the Maximum Ordinality Principle (MOP) and, in addition, in Adherence to the Explicit Solution to the “Three-Body Problem”. In this way the Methodology here proposed is able to suggest how to account for the synergistic effects of the various drugs, especially when the latter are characterized by different concentrations and, at the same time, by generally different half-lives respectively.
文摘This paper presents the Solution to the “Three-body Problem” in the Light of the Maximum Ordinality Principle. In the first part, however, it starts with the Solution to the Solar System, made up of “11 Bodies”. This is because, in such a context, the “Three-body Problem” can be analyzed in its all descriptive possibilities. Nonetheless, the paper also presents the Solution to the “Three-body Problem” with reference to Systems totally independent from the Solar System, such as, for example, the “Triple Stars” and the “Triple Galaxies”. In this way, the paper offers a sufficiently complete framework concerning the Solution to the “Three-body Problem”, always in the Light of the Maximum Ordinality Principle, described in detail in Appendix A.
基金supported by the National Natural Science Foundation of China (60904059 60975049)+1 种基金the Philosophy and Social Science Foundation of Hunan Province (2010YBA104)the National High Technology Research and Development Program of China (863 Program)(2009AA04Z107)
文摘A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making (MCGDM) and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.
基金This work was supported in part by the Natural Science Foundation of Shanghai(21ZR1403600)the National Natural Science Foundation of China(62176059)+3 种基金Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)Zhang Jiang Laboratory,Shanghai Sailing Program(21YF1402800)Shanghai Municipal of Science and Technology Project(20JC1419500)Shanghai Center for Brain Science and Brain-inspired Technology.
文摘The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)loss.However,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better generalization.To improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.The merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)module.First,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree.Hence,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction variance.Second,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature perturbation.During training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix.Experimental results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(No.50974010)the Beijing Natural Science Foundation(No.2162036)the National Training Program of Innovation and Entrepreneurship for Undergraduates(No.202010004006)。
文摘In the preparation of plasma electrolytic oxidation(PEO)coating,the rapid heating of freely-happened electron avalanche under traditional discharge(TD)mode inevitably results in a strong eruption of electric breakdown melt.The PEO coating is loose and invariably composed of a very thin inner dense layer and an outer loose layer,as a result of which its properties and application have been limited greatly.In this work,for purpose of weakening the eruption of breakdown melt,thickening the inner dense layer,densifying the outer loose layer and improving the performance of PEO coating,ordinal discharge(OD)mode of PEO coating is developed by regulating the mass ratio of MgF_(2) to MgO(α)and voltage in the PEO investigation on AZ61 magnesium alloy in KF-KOH electrolyte.The formation mechanism under different discharge mode,electrochemical corrosion and wear of PEO coatings are investigated.The results show that the suitableαand voltage for effective OD are 1.3 and 130 V under which the freely-happened electron avalanche in MgF_(2) under TD mode can be restricted by the adequate adjacent MgO.Compared with TD mode,the inner dense layer,in which the(10¯1)plane of MgF_(2) is parallel to the(111)plane of MgO at their well-knit semi-coherent interface,is thickened to 2.4∼7.2 times,the corrosion potential(E_(corr))improvement is enlarged to 3.6∼13.2 times and the corrosion current intensity(I_(corr))is reduced from 10.8∼9.499 to 0.433(10^(−6) A/cm^(2)).The outer loose layer is densified and the wear rate is lessened 65.5%∼89.8%by the evident melioration in surface porosity,impedance and hardness.This work deepens the understanding about the discharge of PEO coating and provides an available OD mode for preparing excellent PEO coating.
文摘The present paper aims at showing how it is possible to requalify the structures of an urban system, in order to increase its resistance and its correlative resilience, against natural calamities (earthquakes, hurricanes, etc.), by adopting as reference criterion the Maximum Ordinality Principle (MOP). In this sense, the paper opens a radically new perspective in this field. In fact, the village assumed as a case study was modelled as a Self-Organizing System. This is because, although the village is usually considered as being solely made of buildings, streets, places and so on, in reality it has been conceived, planned and realized by human beings during several centuries. In addition, the people who actually leave in such an urban center, systematically deal with its maintenance, in order to possibly increase its functionality. This justifies the assumption of the village as being a Self-Organizing System and, consequently, it has been analyzed in the light of the MOP, which represents a valid reference principle for analyzing both “non-living”, “living” and “conscious” self-organizing systems.
基金Supported by the High Technology Research and Devel-opment Program of China (No.2006AA01Z150)the Key Project of the National Natural Science Foundation of China (No.60373101)+1 种基金the Natural Science Foundation of Heilongjiang Province (No.F2007-14)the Project of Heilongjiang Outstanding Young University Teacher (No. 1151G037).
文摘This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effec- tiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM sig- nificantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
文摘The adjacent-categories, continuation-ratio and proportional odds logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response data. We propose fitting these models with a logarithmic link to allow estimation of different forms of the risk ratio. Each of the resulting ordinal response log-link models is a constrained version of the log multinomial model, the log-link counterpart of the multinomial logistic model. These models can be estimated using software that allows the user to specify the log likelihood as the objective function to be maximized and to impose constraints on the parameter estimates. In example data with a dichotomous covariate, the unconstrained models produced valid coefficient estimates and standard errors, and the constrained models produced plausible results. Models with a single continuous covariate performed well in data simulations, with low bias and mean squared error on average and appropriate confidence interval coverage in admissible solutions. In an application to real data, practical aspects of the fitting of the models are investigated. We conclude that it is feasible to obtain adjusted estimates of the risk ratio for ordinal outcome data.
基金supported by National Natural Science Foundation of China under Grant 60703013 and 10978011Key Program of National Natural Science Foundation of China under Grant 60932008+1 种基金National Science Fund for Distinguished Young Scholars under Grant 50925625China Postdoctoral Science Foundation.
文摘In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.