441 research outputs found
Recognition of Odor Characteristics Based on BP Neural Network
This paper introduces the basic principle and calculation steps of BP neural network algorithm for classification and prediction of odor characteristic parameters. Using the PEN3 electronic nose collects the volatile components of milk and programming BP neural network algorithm under MATLAB condition. This paper validate the use of BP neural network algorithm on milk quality prediction is effective
Examining Individuals’ Ads Click Intention in the Wechat Moments: A Lens of Elaboration Likelihood Model
Drawing upon elaboration likelihood model (ELM), we compared the dual routesin determining users’ ads click intentions and examined the mediation mechanism of cognitive vs. affective trust on the influence processesin the Wechat Moments. A scenario-based survey was conducted in a university of China, and 183 data was collected. Structural equation modelling analysis was used to test the research model. The empirical results suggestedthat content personalization and social recommendation weresignificant antecedents of ads click intention, and their effects weremediated by cognitive trust and affective trust. Moreover, amulti-group analysis indicatedthat the two influence processes weremoderated by prior product experience. Theoretical and practical implications areillustrated in the final section
Energy-Efficient Power Control for Multiple-Task Split Inference in UAVs: A Tiny Learning-Based Approach
The limited energy and computing resources of unmanned aerial vehicles (UAVs)
hinder the application of aerial artificial intelligence. The utilization of
split inference in UAVs garners significant attention due to its effectiveness
in mitigating computing and energy requirements. However, achieving
energy-efficient split inference in UAVs remains complex considering of various
crucial parameters such as energy level and delay constraints, especially
involving multiple tasks. In this paper, we present a two-timescale approach
for energy minimization in split inference, where discrete and continuous
variables are segregated into two timescales to reduce the size of action space
and computational complexity. This segregation enables the utilization of tiny
reinforcement learning (TRL) for selecting discrete transmission modes for
sequential tasks. Moreover, optimization programming (OP) is embedded between
TRL's output and reward function to optimize the continuous transmit power.
Specifically, we replace the optimization of transmit power with that of
transmission time to decrease the computational complexity of OP since we
reveal that energy consumption monotonically decreases with increasing
transmission time. The replacement significantly reduces the feasible region
and enables a fast solution according to the closed-form expression for optimal
transmit power. Simulation results show that the proposed algorithm can achieve
a higher probability of successful task completion with lower energy
consumption
Cooperative Tri-Point Model-Based Ground-to-Air Coverage Extension in Beyond 5G Networks
The utilization of existing terrestrial infrastructures to provide coverage
for aerial users is a potentially low-cost solution. However, the already
deployed terrestrial base stations (TBSs) result in weak ground-to-air (G2A)
coverage due to the down-tilted antennas. Furthermore, achieving optimal
coverage across the entire airspace through antenna adjustment is challenging
due to the complex signal coverage requirements in three-dimensional space,
especially in the vertical direction. In this paper, we propose a cooperative
tri-point (CoTP) model-based method that utilizes cooperative beams to enhance
the G2A coverage extension. To utilize existing TBSs for establishing effective
cooperation, we prove that the cooperation among three TBSs can ensure G2A
coverage with a minimum coverage overlap, and design the CoTP model to analyze
the G2A coverage extension. Using the model, a cooperative coverage structure
based on Delaunay triangulation is designed to divide triangular prism-shaped
subspaces and corresponding TBS cooperation sets. To enable TBSs in the
cooperation set to cover different height subspaces while maintaining ground
coverage, we design a cooperative beam generation algorithm to maximize the
coverage in the triangular prism-shaped airspace. The simulation results and
field trials demonstrate that the proposed method can efficiently enhance the
G2A coverage extension while guaranteeing ground coverage
Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing
Adverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are categorized into 5 types including dry, wet, snow, ice, and water. Then, according to the original image size, images are segmented; 9-dimensional color eigenvectors and 4 texture eigenvectors are extracted to construct road surface state characteristics database. Next, a recognition method of road surface state based on SVM (Support Vector Machine) is proposed. In order to improve the recognition accuracy and the universality, a grid searching algorithm and PSO (Particle Swarm Optimization) algorithm are used to optimize the kernel function factor and penalty factor of SVM. Finally, a large number of actual road surface images in different environments are tested. The results show that the method based on SVM and image segmentation is feasible. The accuracy of PSO algorithm is more than 90%, which effectively solves the problem of road surface state recognition under the condition of hybrid or different video scenes
IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation
During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture
- …