scholarly journals Robust Ordinal Embedding from Contaminated Relative Comparisons

2019 ◽  
Vol 33 ◽  
pp. 7908-7915 ◽  
Author(s):  
Ke Ma ◽  
Qianqian Xu ◽  
Xiaochun Cao
Keyword(s):  
Exact Solution ◽  
Optimal Solutions ◽  
Unified Framework ◽  
Real World Data ◽  
Traditional Methods ◽  
Promising Tool ◽  
Multi Stage ◽  
Relative Comparisons ◽  
Rank Equality ◽  
Embedding Methods

Existing ordinal embedding methods usually follow a twostage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.

Prediction of the Noise From a Multi-Stage Centrifugal Pump

2005 ◽  
Author(s):  
Chisachi Kato ◽  
Shinobu Yoshimura ◽  
Yoshinobu Yamade ◽  
Yu Yan Jiang ◽  
Hong Wang ◽  
...  
Keyword(s):  
Centrifugal Pump ◽  
External Surface ◽  
Pressure Fluctuations ◽  
Internal Surface ◽  
Frequency Spectra ◽  
Eddy Simulation ◽  
Promising Tool ◽  
Multi Stage ◽  
Flow Induced Noise ◽  
Good Agreement

Presented in this paper is a one-way coupled simulation of fluid flow and structural analyses that is applied to the prediction of the noise radiated from the external surface of a 5-stage centrifugal pump. A large eddy simulation is firstly applied to compute pressure fluctuation on the internal surface of the pump. These computed fluctuations are then fed to the structural analysis based on an explicit dynamic finite element method that computes the elastic wave propagating in the solid. The computed pressure fluctuations are compared with measurements in several points in the diffuser passage and a good agreement is obtained in terms of their frequency spectra. The vibration velocities on the external surface of the pump are also compared with the measured equivalents, which show a reasonably good agreement. The proposed method thus seems quite a promising tool for prediction of and reduction in the flow-induced noise generated from hydraulic turbomachinery in general.

Enhancing Recommender Diversity Using Gaussian Cloud Transformation

2015 ◽  
Vol 23 (04) ◽  
pp. 521-544 ◽  
Author(s):  
Jinpeng Chen ◽  
Yu Liu ◽  
Deyi Li
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Data Sets ◽  
Complete Spectrum ◽  
Unified Framework ◽  
Real World Data ◽  
Balance Accuracy ◽  
Average Accuracy ◽  
Novel Method ◽  
Attention To Diversity

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

Comprehensive Semi-Supervised Multi-Modal Learning

2019 ◽  
Author(s):  
Yang Yang ◽  
Ke-Tao Wang ◽  
De-Chuan Zhan ◽  
Hui Xiong ◽  
Yuan Jiang
Keyword(s):  
Real World ◽  
Regularization Method ◽  
Empirical Studies ◽  
Attention Mechanism ◽  
Unified Framework ◽  
Real World Data ◽  
World Data ◽  
Modal Data ◽  
Real World Applications ◽  
Critical Challenge

Multi-modal learning refers to the process of learning a precise model to represent the joint representations of different modalities. Despite its promise for multi-modal learning, the co-regularization method is based on the consistency principle with a sufficient assumption, which usually does not hold for real-world multi-modal data. Indeed, due to the modal insufficiency in real-world applications, there are divergences among heterogeneous modalities. This imposes a critical challenge for multi-modal learning. To this end, in this paper, we propose a novel Comprehensive Multi-Modal Learning (CMML) framework, which can strike a balance between the consistency and divergency modalities by considering the insufficiency in one unified framework. Specifically, we utilize an instance level attention mechanism to weight the sufficiency for each instance on different modalities. Moreover, novel diversity regularization and robust consistency metrics are designed for discovering insufficient modalities. Our empirical studies show the superior performances of CMML on real-world data in terms of various criteria.

MULTI-OBJECTIVE OPTIMIZATION OF THERMO-ECOLOGICAL CRITERIA BASED PERFORMANCE PARAMETERS OF REHEAT AND REGENERATIVE BRAYSSON CYCLE

2021 ◽  
pp. 1-36
Author(s):  
R Chandramouli ◽  
G Ravi Kiran Sastry ◽  
S. K. Gugulothu ◽  
M S S Srinivasa Rao
Keyword(s):  
Power Density ◽  
Pressure Ratio ◽  
Optimal Solution ◽  
Coefficient Of Performance ◽  
Optimal Solutions ◽  
Performance Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Stage ◽  
Ecological Criteria

Abstract The reheat and regenerative Braysson cycle being an alternative for combined cycle power plants needs to be optimized for its efficient utilization of energy resources. Therefore, to obtain the best possible overall pressure ratio, regenerator effectiveness and pressure ratio across multi-stage compression in order to simultaneously maximize exergy efficiency, non-dimensional power density and ecological coefficient of performance for three different maximum temperature situations, multi-objective optimization of the above cycle is carried out using Non-dominated sorting genetic algorithm-II (NSGA-II). The optimal solutions given by the Pareto frontier are further assessed through widely used decision makers namely LINMAP, TOPSIS and Bellman-Zadeh techniques. The optimal solutions attained by the decision making process are further evaluated for their deviation from the non-ideal and ideal solutions. The optimal solution obtained through TOPSIS possess the minimum deviation index. Finally the results are authenticated by performing an error analysis. Such optimal scenarios achieved for the three maximum temperatures are further analysed to achieve the final objective of the most optimal solution which happens to be at 1200K. The simultaneous optimization of performance parameters which reflect the thermo-ecological criteria to be satisfied by a power plant has resulted in values of 0.479, 0.327 & 0.922 for exergy efficiency, non-dimensional power density and ecological coefficient of performance respectively. These optimized performance parameters are obtained for an overall pressure ratio of 7.5, regenerator effectiveness of 0.947 and pressure ratio across multi-stage compression of 1.311.

scholarly journals Estimating instantaneous peak flow from mean daily flow

2017 ◽  
Vol 48 (6) ◽  
pp. 1474-1488 ◽  
Author(s):  
Bo Chen ◽  
Witold F. Krajewski ◽  
Fan Liu ◽  
Weihua Fang ◽  
Zongxue Xu
Keyword(s):  
Prediction Error ◽  
Peak Flow ◽  
Snow Melting ◽  
Traditional Methods ◽  
Peak Flows ◽  
Promising Tool

Abstract We revisited three traditional methods and proposed a slope-based method, which all require only mean daily flow (MDF) records as inputs, to estimate instantaneous peak flows (IPFs). We applied these methods to 144 basins in Iowa, USA, with drainage areas in the range 7–220,000 km2. This application involves ∼3,800 peak flow events triggered by snow-melting and rainfall over the period from 1997 to 2014. The results show that: using a sequence of MDF rather than just the maximum MDF improves the accuracy of estimating IPFs from MDFs; Sangal's method tends to overestimate, Fill and Steiner's method works reasonably well and is marginally outperformed by the slope-based method. For the slope-based method, about 75% of the basins have prediction error of IPFs within ±10% and about 85% of the basins within ±20%; performances of the four methods degrade as the basin size decreases. Fill and Steiner's and the slope-based methods work well for basins larger than 500 km2, poorly for basins smaller than 100 km2, and fairly well for basins with sizes in between. Our proposed method is a simple and promising tool to estimate IPFs from MDFs for areas where IPF records are unavailable or are insufficient.

scholarly journals Modeling of complex structured processes using discrete iterative networks and petri nets

2021 ◽  
Vol 2131 (3) ◽  
pp. 032004
Author(s):  
A Korneev ◽  
T Lavrukhina ◽  
T Smetannikova ◽  
Yu Glazkova
Keyword(s):  
Petri Nets ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Processing Mode ◽  
Functional Relationships ◽  
Multi Stage ◽  
Optimal Value ◽  
Spatially Distributed ◽  
Alternative Solutions ◽  
Production Conditions

Abstract The paper considers the use of discrete iterative networks and Petri nets in the modeling of complex structured processes. In general, the production process is represented as a composition of machines. Probabilistic finite machine are modeled to select optimal solutions from a certain set of alternative solutions. Alphabets of finite and probabilistic machines are formed on the basis of discrete optimization methods when modeling multi-stage productions. The processing mode adaptation block is used to change the alphabets when the production conditions are changed. Using the alphabet generation block allows you to choose the optimal value of the alphabets of the random variables under study. During modeling complex systems and developing algorithms for managing them, the presence of ambiguous functional relationships between factors and quality indicators is taken into account. Methods of modeling complex spatially distributed objects based on a hierarchy of machines belonging to a predetermined finite number of machine types using iterative networks and Petri nets are described.

scholarly journals LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yunliang Chen ◽  
Shaoqian Chen ◽  
Nian Zhang ◽  
Hao Liu ◽  
Honglei Jing ◽  
...  
Keyword(s):  
Health Status ◽  
Prediction Model ◽  
Sensor Network ◽  
Transmission Lines ◽  
Power Grid ◽  
Principal Component ◽  
Real World Data ◽  
Traditional Methods ◽  
Efficient Operation ◽  
Mlp Model

The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status of the power grid sensor network. The LPR-MLP model consists of two parts: (1) local binary pattern (LBP), principal component analysis (PCA), and ReliefF are used to process image data and meteorological and mechanical data and (2) the multilayer perceptron (MLP) method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods.

scholarly journals Partial Multi-Label Learning with Noisy Label Identification

2020 ◽  
Vol 34 (04) ◽  
pp. 6454-6461 ◽  
Author(s):  
Ming-Kun Xie ◽  
Sheng-Jun Huang
Keyword(s):  
Ground Truth ◽  
Noise Model ◽  
Learning Approach ◽  
Data Sets ◽  
Trace Norm ◽  
Unified Framework ◽  
Real World Data ◽  
Ground Truth Information ◽  
Label Correlation ◽  
Noisy Labels

Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels. Recent studies usually solve PML problems with the disambiguation strategy, which recovers ground-truth labels from the candidate label set by simply assuming that the noisy labels are generated randomly. In real applications, however, noisy labels are usually caused by some ambiguous contents of the example. Based on this observation, we propose a partial multi-label learning approach to simultaneously recover the ground-truth information and identify the noisy labels. The two objectives are formalized in a unified framework with trace norm and ℓ1 norm regularizers. Under the supervision of the observed noise-corrupted label matrix, the multi-label classifier and noisy label identifier are jointly optimized by incorporating the label correlation exploitation and feature-induced noise model. Extensive experiments on synthetic as well as real-world data sets validate the effectiveness of the proposed approach.

scholarly journals THE MAXIMUM RANGE COLUMN METHOD – GOING BEYOND THE TRADITIONAL INITIAL BASIC FEASIBLE SOLUTION METHODS FOR THE TRANSPORTATION PROBLEMS

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Huzoor Bux Kalhoro
Keyword(s):  
Feasible Solution ◽  
The Other ◽  
Benchmark Problems ◽  
Optimal Solutions ◽  
Basic Feasible Solution ◽  
Traditional Methods ◽  
Transportation Problems ◽  
North West ◽  
Column Method ◽  
Maximum Range

The transportation problems (TPs) are a fundamental case-study topic in operations research, particularly in the field of linear programming (LP). The TPs are solved in full resolution by using two types of methods: initial basic feasible solution (IBFS) and optimal methods. In this paper, we suggest a novel IBFS method for enhanced reduction in the transportation cost associated with the TPs. The new method searches for the range in columns of the transportation table only, and selects the maximum range to carry out allocations, and is therefore referred to as the maximum range column method (MRCM). The performance of the proposed MRCM has been compared against three traditional methods: North-West-Corner (NWCM), Least cost (LCM) and Vogel’s approximation (VAM) on a comprehensive database of 140 transportation problems from the literature. The optimal solutions of the 140 problems obtained by using the TORA software with the modified distribution (MODI) method have been taken as reference from a previous benchmark study. The IBFSs obtained by the proposed method against NWCM, LCM and VAM are mostly optimal, and in some cases closer to the optimal solutions as compared to the other methods. Exhaustive performance has been discussed based on absolute and relative error distributions, and percentage optimality and nonoptimality for the benchmark problems. It is demonstrated that the proposed MRCM is a far better IBFS method for efficiently solving the TPs as compared to the other discussed methods, and can be promoted in place of the traditional methods based on its performance.

scholarly journals A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Audrey Hulot ◽  
Denis Laloë ◽  
Florence Jaffrézic
Keyword(s):  
Breast Cancer ◽  
Simple Procedure ◽  
Data Sets ◽  
Integration Step ◽  
Visual Interpretation ◽  
Data Types ◽  
Unified Framework ◽  
Real World Data ◽  
Multiple Factor Analysis ◽  
Data Set

Abstract Background Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations. Results To this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question. Conclusion Our approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes.

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