Jiabin Yu's research while affiliated with Beijing Technology and Business University and other places

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT). The sensor nodes(SNs) in WSNs are powered by batteries, making energy efficiency and network lifetime key issues in WSNs research. Cluster-routing algorithms are a focal point for addressing energy efficiency challenges. Selecting cluster heads (CHs) based on clustering algorithms can reduce the energy consumption of SNs and enhance overall network stability and sustainability. This paper introduces a method for selecting the number of clusters (\({N_C}\)) and CHs based on fuzzy clustering. The fuzzy C-means (FCM) clustering algorithm requires pre-setting the number of clusters, with no inclusion of CHs information in the output after running the algorithm. The number of clusters and selection of CHs were determined using the elbow rule and scoring criteria for CHs selection. The performance of the network under different monitoring areas is simulated and analyzed in this paper. Experiments demonstrate that the proposed algorithm outperforms existing algorithms in terms of network energy consumption and lifetime.

This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%.

A path planning method for unmanned surface vessels (USV) in dynamic environment is proposed to address the impact of dynamic environments on path planning results and the lack of dynamic obstacle avoidance capabilities. First, the considering ocean current rapidly exploring random tree (RRT*) (COC-RRT*) algorithm was proposed for global path planning. The RRT* algorithm has been enhanced with the integration of the virtual field sampling algorithm and ocean current constraint algorithm. The COC-RRT* algorithm optimizes the global planning path by adjusting the path between the parent nodes and child nodes. Second, according to the limitations of the International Regulations for Preventing Collisions at Sea (COLREGs), the improved dynamic window approach (DWA) is applied for local path planning. To enhance the ability of avoid dynamic obstacles, the dist function in the DWA algorithm has been improved. Simulation experiments were conducted in three scenarios to validate the proposed algorithm. The experimental results demonstrate that, in comparison with other algorithms, the proposed algorithm effectively avoids dynamic obstacles and mitigates the influence of the space-varying ocean current environment on the path-planning outcome. Additionally, the proposed algorithm exhibits high efficiency and robustness. The results verified the effectiveness of the proposed algorithm in dynamic environments.

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.

In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data’s temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities.

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction.

The rice processing chain is an extremely important part of the rice supply chain, so the risk assessment on the main pollutants in the rice processing chain is of great significance. The existing risk assessment methods are subjective on the weight determination of risk indicators and the comprehensive risk assessment of the processing chain, and they do not consider the characteristics of the indicators, resulting in the unreasonable assessment results. And the existing assessment models have poor robustness and low accuracy. To solve these problems, this article proposes a risk assessment method for the entire rice processing chain based on Kmeans + + and extreme learning machine (ELM). Based on multi-level risk assessment index system, a risk assessment model of main pollutants in rice processing chain was constructed. According to the characteristics of rice pollutants, the pollutant index toxicological characteristics were integrated into the entropy weight. The comprehensive risk index of the processing chain was obtained, and the Kmeans + + clustering algorithm was used to cluster the index and adaptively mining data characteristics to classify risk levels. ELM was used for risk assessment. The proposed method was validated by 75 sets of rice processing chain data, based on six pollutant indicators of Pb, Cd, Hg, aflatoxin B1, zearalenone and deoxynivalenol. The results show that the risk classification accuracy of the proposed method in the test set was 93.3%, and it was more accurately and reasonably than the compared methods. This study strengthens the advantages of big data and artificial intelligence technology in food safety and supervision in the process of digital transformation of agriculture, provides more accurate and reliable decision-making basis for food safety supervisory departments, and lays a solid foundation for subsequent rapid warning and prevention and control decisions.

Hybrid sensor networks, which contain mobile nodes and stationary nodes, are being used more and more widely. The second deployment of mobile nodes is a key problem to be solved, and the deployment performance of the network directly affects the monitoring effect of the network. Optimizing the configuration ratio of the two nodes can effectively reduce the network cost. In this paper, under the premise of knowing the coverage of the required monitoring area, the impact of sensor devices on node configuration is studied through parameter analysis, and the number and types of sensors that should be deployed in the hybrid sensor network are deduced, which can be conveniently and accurately used to design the actual hybrid sensor network. At the same time, for the secondary deployment of mobile nodes, this paper proposes a new mobile coverage method BS-CCP (box search and concentric circle positioning) to improve the coverage of the hybrid sensor network and maximize the coverage of the target area with the specified sensor types and numbers. Compared with existing work, the method in this paper reduces the number of iterations and reduces the number of required nodes. Comparing BS-CCP with the existing network mobile coverage algorithm, the experimental results show that the coverage obtained by this method is larger and more efficient.