Rainfall plays a vital role in guiding clothes drying. This paper proposes an intelligent clothes hanger system based on the analysis of rainfall data. The system consists of an electric mechanism, weather monitoring ...
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Rainfall plays a vital role in guiding clothes drying. This paper proposes an intelligent clothes hanger system based on the analysis of rainfall data. The system consists of an electric mechanism, weather monitoring module,wireless communication module, central control module, network server, and user APP. The weather data monitored by the local weather monitoring module is uploaded to the network server in real-time, users can monitor and control the clothes hanger through the APP. Besides, the web server constructs a twodimensional rainfall map based on the weather data provided by the clothes hangers in different geographic locations. Using the weight matrix to perform convolution operation on the rainfall map, the weighted average of the rainfall conditions at any point and its surrounding points is realized. The result is used as the rainfall risk prediction of the area corresponding to this point, used to guide drying. Finally, the feasibility of the algorithm is verified by Labview simulation.
In order to improve the effect of obstacle avoidance, a PID control algorithm combined with genetic algorithm is proposed. By establishing the mathematical model of UAV, the auxiliary control system of UAV is analyzed...
In order to improve the effect of obstacle avoidance, a PID control algorithm combined with genetic algorithm is proposed. By establishing the mathematical model of UAV, the auxiliary control system of UAV is analyzed, and then the control system is optimized by genetic algorithm. Taking STM32 as the control platform, the obstacle avoidance experiment was carried out by building the experimental platform. The experimental results show that the algorithm can avoid obstacles effectively.
Frequent itemset mining is one of the most important data mining tasks. Classical frequent itemset mining algorithms need to store data in a centralized way and run in a batch way, which cannot meet the requirements o...
Frequent itemset mining is one of the most important data mining tasks. Classical frequent itemset mining algorithms need to store data in a centralized way and run in a batch way, which cannot meet the requirements of fast updating big data mining. In this paper, we propose a distributed incremental frequent itemset mining algorithm, DisCANTree, which uses CANTree to store the conditional database, achieves the load balance between nodes by grouping all items, updates the new transaction to the existing CANTree to avoid the load of tree reconstruction, and uses the efficient FPGrowth algorithm to mine CANTree to generate frequent itemsets. The popular distributed programming model mapReduce and its open source system Hadoop are used to implement the DisCANTree algorithm. The experimental results show that the DisCANTree algorithm has more advantages than the most popular PFP algorithm in performance as well as the number of transferred records between nodes, and especially suits for the fast updating sparse big data.
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