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Search Results (18)
  • Open Access

    ARTICLE

    Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

    Omar Abdulghafoor1, Musbah Shaat2, Ibraheem Shayea3, Ahmad Hamood1, Abdelzahir Abdelmaboud4, Ashraf Osman Ibrahim5, Fadhil Mukhlif6,*, Herish Badal1, Norafida Ithnin6, Ali Khadim Lwas7

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3045-3064, 2023, DOI:10.32604/cmc.2023.033888

    Abstract The computational complexity of resource allocation processes, in cognitive radio networks (CRNs), is a major issue to be managed. Furthermore, the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users, CRs, and primary users, PUs, exist in the identical geographical area. Hence, this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarios while limiting interference to PUs to allowable threshold. Hence, this paper, compared to other frameworks proposed in the literature, proposes a two-step approach to reduce the complexity of… More >

  • Open Access

    ARTICLE

    Study on Quantum Finance Algorithm: Quantum Monte Carlo Algorithm based on European Option Pricing

    Jian-Guo Hu1,*, Shao-Yi Wu1,*, Yi Yang1, Qin-Sheng Zhu1, Xiao-Yu Li1, Shan Yang2

    Journal of Quantum Computing, Vol.4, No.1, pp. 53-61, 2022, DOI:10.32604/jqc.2022.027683

    Abstract As one of the major methods for the simulation of option pricing, Monte Carlo method assumes random fluctuations in the distribution of asset prices. Under certain uncertainties process, different evolution paths could be simulated so as to finally yield the expectation value of the asset price, which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations. In order to solve the above computational problem, quantum Monte Carlo (QMC) has been established and applied in the relevant systems such as European call options. In this work, both MC and QM methods are adopted to simulate… More >

  • Open Access

    ARTICLE

    How Load Aggregators Avoid Risks in Spot Electricity Market: In the Framework of Power Consumption Right Option Contracts

    Jiacheng Yang1, Xiaohe Zhai1, Zhongfu Tan1,2,*, Zhenghao He1

    Energy Engineering, Vol.119, No.3, pp. 883-906, 2022, DOI:10.32604/ee.2022.018033

    Abstract There is uncertainty in the electricity price of spot electricity market, which makes load aggregators undertake price risks for their agent users. In order to allow load aggregators to reduce the spot market price risk, scholars have proposed many solutions, such as improving the declaration decision-making model, signing power mutual insurance contracts, and adding energy storage and mobilizing demand-side resources to respond. In terms of demand side, calling flexible demand-side resources can be considered as a key solution. The user's power consumption rights (PCRs) are core contents of the demand-side resources. However, there have been few studies on the pricing… More >

  • Open Access

    ARTICLE

    Estimation of Locational Marginal Pricing Using Hybrid Optimization Algorithms

    M. Bhoopathi1,*, P. Palanivel2

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 143-159, 2022, DOI:10.32604/iasc.2022.017705

    Abstract At present, the restructured electricity market has been a prominent research area and attracted attention. The motivation of the restructuring in the power system is to introduce the competition at various levels and to generate a correct economic signal to reduce the generation cost. As a result, it is required to have an effective price scheme to deliver useful information about the power. The pricing mechanism is dependent on the demand at the load level, the generator bids, and the limits of the transmission network. To address the congestion charges, Locational Marginal Pricing (LMP) is utilized in restructured electricity markets.… More >

  • Open Access

    ARTICLE

    Big Data Knowledge Pricing Schemes for Knowledge Recipient Firms

    Chuanrong Wu1,*, Haotian Cui1, Zhi Lu2, Xiaoming Yang3, Mark E. McMurtrey4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3275-3287, 2021, DOI:10.32604/cmc.2021.019969

    Abstract Big data knowledge, such as customer demands and consumer preferences, is among the crucial external knowledge that firms need for new product development in the big data environment. Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients. This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients: subscription pricing and pay-per-use pricing. We find that: (1) the subscription price of big data knowledge has no effect on the optimal time of knowledge… More >

  • Open Access

    ARTICLE

    Dynamic Resource Pricing and Allocation in Multilayer Satellite Network

    Yuan Li1,7, Jiaxuan Xie1, Mu Xia2, Qianqian Li3, Meng Li4, Lei Guo5,*, Zhen Zhang6

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3619-3628, 2021, DOI:10.32604/cmc.2021.016187

    Abstract The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed to maximize the market advantage… More >

  • Open Access

    ARTICLE

    Dynamic Pricing Model of E-Commerce Platforms Based on Deep Reinforcement Learning

    Chunli Yin1,*, Jinglong Han2

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 291-307, 2021, DOI:10.32604/cmes.2021.014347

    Abstract With the continuous development of artificial intelligence technology, its application field has gradually expanded. To further apply the deep reinforcement learning technology to the field of dynamic pricing, we build an intelligent dynamic pricing system, introduce the reinforcement learning technology related to dynamic pricing, and introduce existing research on the number of suppliers (single supplier and multiple suppliers), environmental models, and selection algorithms. A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions. The first step is to analyze the pricing strategies of e-commerce… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

    Jinseok Kim1, Babar Shah2, Ki-Il Kim3,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042

    Abstract Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.… More >

  • Open Access

    ARTICLE

    A Negotiated Pricing Model for Innovation Services Based on the Multiobjective Genetic Algorithm

    Yan Zhou1,*, Yue Li1, Yunxing Zhang2

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 191-203, 2021, DOI:10.32604/iasc.2021.014142

    Abstract Service pricing is a bottleneck in the development of innovation services, as it is the issue of most concern to the suppliers and demanders. In this paper, a negotiated pricing model that is based on the multiobjective genetic algorithm is developed for innovation services. Regarding the process of service pricing as a multiobjective problem, the objective functions which include the service price, service efficiency, and service quality for the suppliers and the demanders are constructed. Because the solution of a multiobjective problem is typically a series of alternatives, an additional negotiation process is necessary in determining the final decision. A… More >

  • Open Access

    ARTICLE

    Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

    Hongming Yang1,*, Qian Yu1, Xiao Huang1, Ben Niu2, Min Qi3

    Energy Engineering, Vol.118, No.2, pp. 285-301, 2021, DOI:10.32604/EE.2021.013734

    Abstract The two-way interaction between smart grid and customers will continuously play an important role in enhancing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources. Thus far, the existing electricity pricing mechanisms hardly match the technical properties of smart grid; neither can they facilitate increasing end users participating in the electricity market. In this paper, several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants, in which the mechanisms behind are compatible with demand response operation of end users… More >

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