Visible light communication (VLC) is considered an effective complementary solution for indoor wireless communication to achieve high-speed and secure data transmission. In the random access process of VLC, the collis...
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Visible light communication (VLC) is considered an effective complementary solution for indoor wireless communication to achieve high-speed and secure data transmission. In the random access process of VLC, the collision problem caused by hidden terminals is prominent. The multiple packet reception (MPR) technique can effectively mitigate the multi-terminal collision in VLC and improve the performance and reliability of the system. We proposed, considering the MPR-capable uplink VLC system, a quality of service (QoS)-constrained admission control scheme based on the improved coati optimization algorithm (COA) to guarantee delay requirement. First, we adopt the ALOHA mechanism as the basis of the service, and the arrival stream consists of a primary arrival stream and a background arrival stream. Then, we evaluate the delay performance using the large deviation theory, and the optimization problem with the objective of maximizing the arrival rate is constructed. Finally, we incorporate a cat chaotic map, Levy flight strategy, and t-distribution mutation strategy to improve COA, which makes solving the optimization problem more efficient. Simulation results show that the proposed algorithm is dependable and can improve the system resource utilization.
In the integrated energy systems (IESs), multiple energy sources are coupled, and their spatiotemporal characteristics are different, making the optimal scheduling of the IES extremely difficult. Considering the impac...
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In the integrated energy systems (IESs), multiple energy sources are coupled, and their spatiotemporal characteristics are different, making the optimal scheduling of the IES extremely difficult. Considering the impact of the randomness of wind power and photovoltaic output on the scheduling plan, an optimal scheduling method of day-ahead, intra-day, and real-time correction for IES is proposed. Firstly, random scenarios of wind power and photovoltaic output are generated based on kernel density estimation and copula function. Secondly, under the optimal scenario, the day-ahead optimal scheduling model is established with the lowest total operating cost of IES as the objective function. For intra-day scheduling, the objective function is to minimize the sum of penalty costs for wind and photovoltaic power abandonment, energy storage equipment, each equipment power change, and the change of power supply. Moreover, considering the difference in response speed of cooling, heating, and power, the power-type energy storage is used to realize short-time power dispatching, and the optimization model of real-time correction is established. Finally, the improved coati optimization algorithm (COA) is used to solve the problem. The simulation and experimental results validate the effectiveness and feasibility of the proposed strategy.
Disabled persons demanding healthcare is a developing global occurrence. The support in longer-term care includes nursing, intricate medical, recovery, and social help services. The price is large, but advanced techno...
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Disabled persons demanding healthcare is a developing global occurrence. The support in longer-term care includes nursing, intricate medical, recovery, and social help services. The price is large, but advanced technologies can aid in decreasing expenditure by certifying effective health services and enhancing the superiority of life. The transformative latent of the Internet of Things (IoT) prolongs the existence of nearly one billion persons worldwide with disabilities. By incorporating smart devices and technologies, the IoT provides advanced solutions to tackle numerous tasks challenged by individuals with disabilities and promote equality. Human activity detection methods are the technical area which studies the classification of actions or movements an individual achieves over the recognition of signals directed by smartphones or wearable sensors or over images or video frames. They are efficient in certifying functions of detection of actions, observing crucial functions, and tracking. Conventional machine learning and deep learning approaches effectively detect human activity. This study develops and designs a metaheuristic optimization-driven ensemble model for smart monitoring of indoor activities for disabled persons (MOEM-SMIADP) model. The proposed MOEM-SMIADP model concentrates on detecting and classifying indoor activities using IoT applications for physically challenged people. First, data preprocessing is performed using min-max normalization to convert input data into useful format. Furthermore, the marine predator algorithm is employed in feature selection. For the detection of indoor activities, the proposed MOEM-SMIADP model utilizes an ensemble of three classifiers, namely the graph convolutional network model, long short-term memory sequence-to-sequence (LSTM-seq2seq) method, and convolutional autoencoder. Eventually, the hyperparameter tuning is accomplished by an improved coati optimization algorithm to enhance the classification outcomes of
In order to achieve the economic consumption of renewable energy in a multi-energy power system including wind/PV/hydropower and energy storage, a two-tier coordinated optimal scheduling method based on generative adv...
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In order to achieve the economic consumption of renewable energy in a multi-energy power system including wind/PV/hydropower and energy storage, a two-tier coordinated optimal scheduling method based on generative adversarial network (GAN) scenario generation is proposed in this paper. First, an upper-tier optimization model for the operation of the load and storage system is established to achieve the objective of minimizing the load fluctuation and the cost of energy storage plants. Furthermore, a lower-tier optimization model to minimize the system operation cost and tide risk is established for the optimization operation of renewable energy generation. Second, an improved generative adversarial network is proposed to generate the operation scenes for evaluating the uncertainty characteristics of the wind and photovoltaic (PV) generation. Then, an improved coati optimization algorithm (COA) is used to solve the proposed optimization problem. Finally, the IEEE 30-bus system is selected as the example system for verifying the proposed method. The simulation results corroborate the validity and feasibility of the proposed method.
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