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An Interval-Valued Intuitionistic Hesitant Fuzzy Methodology and Application

NEW GENERATION COMPUTING 2021年第2期39卷 377-407页

作者： Bharati, Shailendra Kumar Univ Delhi Kamala Nehru Coll Dept Math New Delhi 110049 India

Using advantages of interval-valued intuitionistic hesitant fuzzy sets (IVIHFS) for describing the hesitant and intuitionistic decisions of experts and identifying the limitations of previous research works about optimization techniques, this paper introduces a new optimization technique and provides a new computational algorithm, applicable in various real life multiobjective optimization problem (MOOP) of engineering and management sectors, and for this, a new operation between IVIHFSs is first introduced. On the basis of this concept, a stepwise computational algorithm is constructed, and it is an extension of both fuzzy and intuitionistic fuzzy optimization techniques. Finally, the proposed algorithm is illustrated using a production planning problem, and the obtained results are compared with the existing optimization techniques.

关键词： Fuzzy sets Hesitant fuzzy sets linear programming Multiple objective Uncertainty and hesitation

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Effect of Weighting Strategies on Taguchi-Based Optimization of the Four-Stage Constant Current Charge Pattern

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第5期57卷 2704-2714页

作者： Lee, Chien-Hsing Hsu, Chong-Yu Hsu, Shih-Hsien Jiang, Joe-Air Natl Cheng Kung Univ Dept Syst & Naval Mechatron Engn Tainan 701 Taiwan Natl Taiwan Univ Dept Biomechatron Engn Taipei 106 Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung 40447 Taiwan

This article proposes an experimental test to examine the effect of weighting strategies on Taguchi-based optimization of the four-stage constant current (4SCC) charging method. The performance objectives used in the test include charging time, normalized discharge capacity, charging efficiency, average cell temperature rises, and energy losses. To simplify the optimization problem, a scalarization approach incorporating multiobjective functions into a single solution is utilized in this article. There are two types of weights in the scalarization: equal and unequal weights. In the equal weighting strategy, each performance objective is equally important. In the unequal weighting strategy, by contrast, unequal weights represent the performance priority of an objective function. To investigate the effect of the equal weighting strategy, an equal weight of 1/5 is given to each performance objective. For the unequal weighting strategy, this article adopts a random integer ranging from 1 to 5, and a floating-point value from 0 to 1, so the sum of all weights will be equal to 1. The test utilizes Sanyo UR14500P batteries in the cylindrical shape with a nominal voltage of 3.7 V and a rated capacity of 840 mAh. The experimental results show that the charging efficiency remains approximately the same regardless of which weighting strategy is adopted. The equal-weighted 4SCC charging yields the largest nominal charged capacity (96.7%), the longest charging time (102 min), and the smallest average cell temperature rise (1.714 degrees C). Considering charging time reduction, however, the performance of the unequal-weighted 4SCC charging is significantly better than that of the equal-weighted 4SCC charging. In addition, the energy losses brought by applying the equal- and unequal-weighted 4SCC charging are roughly the same.

关键词： Optimization Mathematical model Discharges (electric) linear programming Energy loss Protocols Lithium-ion batteries Constant current constant voltage multistage constant current charge method optimal charge current pattern Taguchi method

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A Refined Load Distribution Method for Large Hydropower Stations With Multiple Units Considering Constraints of Substations

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IEEE ACCESS 2021年 9卷 154524-154539页

作者： Chen, Xiao Zhou, Jianzhong Jia, Benjun Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Peoples R China

Large hydropower stations often undertake peak regulation tasks during the non-abandoning water season. This requires a reasonable arrangement of unit commitment and load distribution (UCLD). In this study, a load distribution model considering constraints of substations was established, and a novel refined and practical method (RPM) was proposed by considering a whole plant-substation module, substation-unit module, and adjustment module along with practical strategies. The Three Gorges hydropower station in China was selected to demonstrate the effects of the RPM. The results showed that the RPM with high calculation timeliness can obtain UCLD schemes with high rationality and practicability under the constraints of the whole plant, substations, and units. The relative mean absolute error of the simulation outflow could be controlled within 1%, thereby providing a valuable reference for forecasting the outflow process which involving the unit level.

关键词： Substations Hydroelectric power generation Dispatching Load modeling Layout Regulation linear programming Large hydropower stations load distribution substation constraints output adjustment strategies bus connection switching status

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Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing With Demand Learning

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IEEE ACCESS 2021年 9卷 88478-88497页

作者： Elreedy, Dina Atiya, Amir F. Shaheen, Samir, I Cairo Univ Comp Engn Dept Giza 12613 Egypt

Dynamic pricing is a beneficial strategy for firms seeking to achieve high revenues. It has been widely applied to various domains such as the airline industry, the hotel industry, and e-services. Dynamic pricing is basically the problem of setting time-varying prices for a certain product or service for the purpose of optimizing revenue. However, a major challenge encountered when applying dynamic pricing is the lack of knowledge of the demand-price curve. The demand-price curve defines the customer's response towards changing the price. In this work, we address the dynamic pricing problem in case of unknown demand-price relation. This work introduces a less myopic pricing approach based on looking ahead for one or several future steps in our quest to optimize revenue. Specifically, the proposed formulation maximizes the summation of the immediate revenue and the expected future revenues of one or multiple look-ahead steps. A key benefit of the proposed approach is that it automatically strikes a balance between the conflicting goals of revenue maximization and demand learning, by producing less myopic and profitable prices. We provide a formulation for the presented look-ahead pricing approach, and we implement two variants of it: one-step and two-step look-ahead methods. Experiments are conducted on synthetic and real datasets to compare the proposed pricing methods to other pricing strategies in literature. The experimental results indicate that the proposed look-ahead methods outperform their counterparts in terms of the achieved revenue gain.

关键词： Pricing Computational modeling Optimization linear regression Operations research linear programming Industries Demand learning dynamic pricing optimization revenue management sequential decision making

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An optimal transport approach for computing adversarial training lower bounds in multiclass classification

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19219-19263页

作者： Nicolás García Trillos Matt Jacobs Jakwang Kim Matthew Werenski Department of Statistics University of Wisconsin-Madison Madison Wisconsin Department of Mathematics UC Santa Barbara Isla Vista CA Department of Mathematics University of British Columbia Vancouver British Columbia Canada Department of Computer Science Tufts University College Avenue Medford MA

Despite the success of deep learning-based algorithms, it is widely known that neural networks may fail to be robust. A popular paradigm to enforce robustness is adversarial training (AT), however, this introduces many computational and theoretical difficulties. Recent works have developed a connection between AT in the multiclass classification setting and multimarginal optimal transport (MOT), unlocking a new set of tools to study this problem. In this paper, we leverage the MOT connection to propose computationally tractable numerical algorithms for computing universal lower bounds on the optimal adversarial risk and identifying optimal classifiers. We propose two main algorithms based on linear programming (LP) and entropic regularization (Sinkhorn). Our key insight is that one can harmlessly truncate the higher order interactions between classes, preventing the combinatorial run times typically encountered in MOT problems. We validate these results with experiments on MNIST and CIFAR-10, which demonstrate the tractability of our approach.

关键词： adversarial learning optimization linear programming Sinkhorn algorithm multiclass classification optimal transport multimarginal optimal transport Wasserstein barycenter generalized barycenter problem

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Irrigation pipe distribution network optimization with Jaya Algorithm: a hybrid approach

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WATER SUPPLY 2021年第7期21卷 3570-3583页

作者： Gajghate, Preeti Walmik Mirajkar, Ashwini B. Visvesvaraya Natl Inst Technol Nagpur 440010 Maharashtra India

The study demonstrates the implementation of Jaya Algorithm (JA) to optimize the Irrigation Pipe Distribution Network (IPDN) for the networks of the Kanhan Branch of Pench project, Maharashtra, India. In the present work, two case studies with their networks of two different sizes are designed using the Critical Path Method (CPM). The pipe diameters thus obtained in CPM are optimized using two optimization techniques, viz. linear programming (LP) and recently developed Jaya Algorithm (JA). JA is a relatively new optimization technique requiring minimum input parameters and are selected based on sensitivity analysis. The comparison of the results using LP and JA exhibits significant reduction in cost of IPDN using newly developed JA. The scope of reduction in the total cost using JA increases with increase in the network area.

关键词： irrigation pipe distribution networks Jaya Algorithm linear programming optimization sensitivity analysis

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Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第8期40卷 2080-2091页

作者： Jiang, Gongfa Wei, Jun Xu, Yuesheng He, Zilong Zeng, Hui Wu, Jiefang Qin, Genggeng Chen, Weiguo Lu, Yao Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Guangdong Peoples R China Percept Vis Med Technol Co Guangzhou 510275 Peoples R China Univ Michigan Dept Radiol Ann Arbor MI 48109 USA Sun Yat Sen Univ Guangdong Key Lab Computat Sci Guangzhou 510275 Guangdong Peoples R China Old Dominion Univ Dept Math & Stat Norfolk VA 23529 USA Southern Med Univ Nanfang Hosp Dept Radiol Guangzhou 510515 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510275 Guangdong Peoples R China

Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM using current DBT/FFDM combo images. Gradient guided conditional generative adversarial networks (GGGAN) objective function was designed to preserve subtle MCs and the perceptual loss was exploited to improve the performance of the proposed DCNN on perceptual quality. We used various image quality criteria for evaluation, including preserving masses and MCs which are important in mammogram. Experiment results demonstrated progressive performance improvement of network using different objective functions in terms of those image quality criteria. The methodology we exploited in the SDM generation task to analyze and progressively improve image quality by designing objective functions may be helpful to other image generation tasks.

关键词： Two dimensional displays Image edge detection Biomedical imaging linear programming Mammography Image reconstruction Three-dimensional displays Breast cancer digital breast tomosynthesis image synthesis deep learning generative adversarial networks

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Uncertainty-Aware Resource Provisioning for Network Slicing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第1期18卷 79-93页

作者： Luu, Quang-Trung Kerboeuf, Sylvaine Kieffer, Michel Nokia Bell Labs F-91620 Nozay France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & SystL2S F-91192 Gif Sur Yvette France Nokia Bell Labs ENSA Lab F-91620 Nozay France

Network slicing allows Mobile Network Operators to split the physical infrastructure into isolated virtual networks (slices), managed by Service Providers to accommodate customized services. The Service Function Chains (SFCs) belonging to a slice are usually deployed on a best-effort premise: nothing guarantees that network infrastructure resources will be sufficient to support a varying number of users, each with uncertain requirements. Taking the perspective of a network Infrastructure Provider (InP), this article proposes a resource provisioning approach for slices, robust to a partly unknown number of users with random usage of the slice resources. The provisioning scheme aims to maximize the total earnings of the InP, while providing a probabilistic guarantee that the amount of provisioned network resources will meet the slice requirements. Moreover, the proposed provisioning approach is performed so as to limit its impact on low-priority background services, which may co-exist with slices in the infrastructure network. Taking all these constraints into account leads to an integer programming problem with many nonlinear constraints. These constraints are first relaxed to get an integer linear programming formulation of the slice resource provisioning problem. This problem is then solved considering the slice resource provisioning demands jointly. A suboptimal approach is finally proposed where slice resource provisioning demands are considered sequentially. Both solutions are compared to provisioning schemes that do not account for best-effort services sharing the common infrastructure network, as well as uncertainties in the slice resource demands.

关键词： Uncertainty Network slicing Resource management Indium phosphide III-V semiconductor materials Quality of service Dynamic scheduling Network slicing resource provisioning uncertainty wireless network virtualization 5G linear programming

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Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 284-288页

作者： Nonomura, Taku Ono, Shunsuke Nakai, Kumi Saito, Yuji Tohoku Univ Dept Aerosp Engn Sendai Miyagi 9808579 Japan Tokyo Inst Technol Sch Comp Yokohama Kanagawa 80305 Japan

The randomized subspace Newton convex methods for the sensor selection problem are proposed. The randomized subspace Newton algorithm is straightforwardly applied to the convex formulation, and the customized method in which the part of the update variables are selected to be the present best sensor candidates is also considered. In the converged solution, almost the same results are obtained by original and randomized-subspace-Newton convex methods. As expected, the randomized-subspace-Newton methods require more computational steps while they reduce the total amount of the computational time because the computational time for one step is significantly reduced by the cubic of the ratio of numbers of randomly updating variables to all the variables. The customized method shows superior performance to the straightforward implementation in terms of the quality of sensors and the computational time.

关键词： Convergence Sparse matrices Signal processing algorithms linear programming Newton method Heuristic algorithms Computational efficiency Randomized subspace Newton algorithm convex sensor selection problem data-driven sensor selection

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Approximate Dynamic programming Decompositions for Economic Dispatch in Microgrids

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IFAC-PapersOnLine 2022年第9期55卷 262-267页

作者： Mikhail V. Goubko Nikita I. Shushko

Three novel approximate dynamic programming algorithms based on the temporal, spatial, and spatiotemporal decomposition are proposed for the economic dispatch problem (EDP) in a distribution energy system with complex topology and many non-dispatchable renewable energy sources and energy storage systems (ESS). Computational efficiency of the proposed algorithms is compared and convergence to the optimal solution is shown in numeric experiments on the example of the two-day hourly EDP for the IEEE 33bw test network having 200+ consumers, 150+ energy storages, and 1000+ consuming devices.

关键词： approximate dynamic programming distribution network economic dispatch energy storage system energy system planning linear programming renewables’ generation spatial decomposition spatiotemporal decomposition temporal decomposition

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