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921
Distributed congestion control strategy using network utility maximization theory in VANET
Published 2019-02-01“…Cooperative vehicle safety system (CVSS) rely on periodical beacons to track neighboring vehicles.High traffic density often causes channel congestion,seriously damaging the performance of CVSS.Existing congestion control strategies aim to ensure the performance in network layer,without considering the service requirements of vehicles in different driving contexts.To solve the problem,a distributed congestion control strategy using network utility maximization (NUM) theory was proposed.First of all,the NUM model for channel resource allocation was introduced.A utility function reflecting vehicle’s safety requirements was proposed in the model.Then under the condition of fixed transmit powers,a optimization problem of channel resource allocation was proposed.Lastly,to solve the optimization problem,a distributed congestion control algorithm named utility-based rate congestion control (UBRCC) algorithm was designed,the algorithm worked out the optimal beaconing rate by updating vehicle’s congestion price,realizing the resource allocation according to vehicle’s safety requirements.Simulation results validate that UBRCC algorithm can efficiently control channel congestion,reduce transmission delay,ensure reliable data transmission and satisfies the requirements of safety applications.…”
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922
Multi-Stage Data-Driven Framework for Customer Journey Optimization and Operational Resilience
Published 2025-03-01“…To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. …”
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923
Does managing a retirement portfolio via rate of return, Sharpe ratio and social interaction generate good returns? An analysis for the years 2017 to 2020
Published 2024-10-01“…Innovatively, the machine learning algorithm optimizes the portfolio allocation using reinforcement learning, which rewards good decisions and punishes bad decisions based on individual and social criteria. …”
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924
Group Stable Matching Problem in Freight Pooling Service of Vehicle–Cargo Matching Platform
Published 2025-06-01“…As a result, they significantly improve the intelligence and precision of logistics resource allocation. However, at the current stage, vehicle–cargo matching platforms rarely promote freight pooling services, leading to resource underutilization. …”
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925
Joint Caching and Computation in UAV-Assisted Vehicle Networks via Multi-Agent Deep Reinforcement Learning
Published 2025-06-01“…This requires balancing system energy consumption and resource allocation fairness while maximizing cache hit rate and minimizing task latency. …”
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926
A Deep Learning Model for ERP Enterprise Financial Management System
Published 2022-01-01“…The proposed model can help enterprises organize their limited resources, realize the scientific allocation of enterprise resources, and create more benefits for enterprises.…”
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927
Efficient Priority Access to the Shared Commercial Radio with Offloading for Public Safety in LTE Heterogeneous Networks
Published 2014-01-01“…The results of the simulation showed that the allocation of radio resources via the courteous mechanism with offloading of new bearers to small cells reduces the rate of blocking bearers and delays the preemption of active bearers present in the LTE HetNets. …”
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928
Research on Stacking Distribution of Steel Plates Input Based on Improved Multi-objective Particle Swarm Optimization
Published 2025-07-01“…The inventory balance index was established based on the standard deviation of the number of steel plates allocated to each reservoir to fully mobilize and balance material storage resources during operation. …”
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929
OPTIMASI PENJADWALAN PROYEK DENGAN PENYEIMBANGAN BIAYA MENGGUNAKAN KOMBINASI CPM DAN ALGORITMA GENETIKA
Published 2012-03-01“…The allocation of activities is determined based on the earliest start time and latest start time by taking into account the cost of resources in each period of the project. …”
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930
Optimization strategies in NOMA-based vehicle edge computing network
Published 2021-03-01“…Nowadays, vehicular network is confronting the challenges to support ubiquitous connections and vast computation-intensive and delay-sensitive smart service for numerous vehicles.To address these issues, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are considered as two promising technologies by letting multiple vehicles to share the same wireless resources, and the powerful edge computing resources were adopted at the edge of vehicular wireless access network respectively.A NOMA-based vehicular edge computing network was studied.Under the condition of guaranteeing task processing delay, the joint optimization problem of task offloading, user clustering, computing resource allocation and transmission power control was proposed to minimize the task processing cost.Since the proposed problem was difficult to solve, it was divided into sub-problems, and a low-complexity and easy-to-implement method was proposed to solve it.The simulation results show that compared with other benchmark algorithms, the proposed algorithm performs well in minimizing costs.…”
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931
Optimization strategies in NOMA-based vehicle edge computing network
Published 2021-03-01“…Nowadays, vehicular network is confronting the challenges to support ubiquitous connections and vast computation-intensive and delay-sensitive smart service for numerous vehicles.To address these issues, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are considered as two promising technologies by letting multiple vehicles to share the same wireless resources, and the powerful edge computing resources were adopted at the edge of vehicular wireless access network respectively.A NOMA-based vehicular edge computing network was studied.Under the condition of guaranteeing task processing delay, the joint optimization problem of task offloading, user clustering, computing resource allocation and transmission power control was proposed to minimize the task processing cost.Since the proposed problem was difficult to solve, it was divided into sub-problems, and a low-complexity and easy-to-implement method was proposed to solve it.The simulation results show that compared with other benchmark algorithms, the proposed algorithm performs well in minimizing costs.…”
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932
An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing
Published 2025-07-01“…However, developing effective workflow scheduling algorithms to find optimal or near-optimal task-to-VM allocation solutions that meet users’ specific QoS requirements still remains an open area of research. …”
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933
Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth
Published 2024-10-01“…Assist yield projections may provide governments and policymakers with valuable information to make well-informed choices about food security, import–export policies, and resource allocation. It facilitates national- and regional-level food supply planning. …”
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934
Deep Learning-Driven Geospatial Modeling of Elderly Care Accessibility: Disparities Across the Urban-Rural Continuum in Central China
Published 2025-04-01“…Taking Changsha as a case study, this research constructs an accessibility evaluation system based on the 15-min life circle theory, utilizing multi-source data. Spatial weighting characteristics of elderly care facility locations were analyzed through machine learning algorithms, and service coverage disparities between urban districts and suburban towns were assessed under 5-, 10-, and 15-min walking thresholds. …”
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935
Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment
Published 2022-01-01“…Compared with the linear search algorithm, the algorithm proposed in this paper can significantly reduce the computation time.…”
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936
Hybrid Optimization Machine Learning Framework for Enhancing Trust and Security in Cloud Network
Published 2024-01-01“…For resource allocation, the framework employs the Time-aware modified best fit decreasing (T-MBFD) algorithm, which adapts to fluctuating workloads. …”
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937
Temporal dependent rate-distortion optimization based on distortion backward propagation
Published 2022-12-01“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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938
Temporal dependent rate-distortion optimization based on distortion backward propagation
Published 2022-12-01“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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939
Dynamic Water Scheduling in the Northwest River Delta Basin Based on Minimum Discharge Flow Control in Cross-section
Published 2025-01-01“…The whale optimization algorithm was used to find the optimal water allocation plan. …”
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940
Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks
Published 2014-01-01“…However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. …”
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