No abstract

The Apriori algorithm is one of the most well-known and widely accepted method for the association rule mining. In Apriori, it uses a prefix tree to represent k-itemsets, generates k-itemset candidates based on the frequent (k-1)-itemsets, and determines the frequent k-itemsets by traversing the prefix tree iteratively based on the transaction records. When k is small, the execution of Apriori is very efficient. However, the execution of Apriori could be very slow when k becomes large because of the deeper recursion depth to determine the frequent k-itemsets. From the perspective of graph computing, the transaction records can be converted to a graph G (V, E), where V is the set of vertices of G that represents the transaction records and E is the set of edges of G that represents the relations among transaction records. Each k-itemset in the transaction records will have a corresponding connected component in G. The number of vertices in the corresponding connected component is the support of the k-itemset. Since the time to find the corresponding connected component of a k-itemset in G is constant for any k, the graph computing method will be very efficient if the number of k-itemsets is relatively small. Based on Apriori and graph computing techniques, a hybrid method, called ANG (Apriori and Graph Computing), is proposed to compute the frequent itemsets. Initially, ANG uses Apriori to compute the frequent k-itemsets and then switches to the graph computing method when k becomes large (where the number of k-itemset candidates is relatively small). The experimental results show that ANG outperforms both Apriori and the graph computing method for all test cases.

The proposed research is based on a real plastic injection factory for cutting board production. Most existing approaches for smart manufacturing tried to build the total solution of IoT by moving forward to the standard of industry 4.0. Under the cost considerations, this will not be acceptable to most factories, so we proposed the vision based technology to solve their immediate problem. Real-time machine condition monitoring is important for making great products and measuring line productivity or factory productivity. The study focused on a vision-based data reader (VDR) in edge computing for smart factories. A simple camera embedded in Field Programmable Gate Array (FPGA) was attached to monitor the screen on the control panel of the machines. Each end device was preprogrammed to capture images and process data on its own. The preprocessing step was then performed to have the normalized illumination of the captured image. A saliency map was generated to detect the required region for recognition. Finally, digit recognition was performed and the recognized digits were sent to the IoT system. The most significant contribution of the proposed VDR system used the compact deep learning model for training and testing purposes to fit the requirement of cost consideration and real-time monitoring in edge computing. To build the compact model, different convolution filters were tested to fit the performance requirement. Experimentations on a real plastic cutting board factory showed the improvement in manufacturing products by the proposed system and achieved a high digit recognition accuracy of 97.56%. In addition, the prototype system had low power and low latency advantages.

In the application of the Internet of Things (IoT) technology, devices may passively receive signals for service or proactively provide data. The main problem in the communication mechanism for an IoT device is how to use the communication mechanism to trigger the wakeup device to exchange data. In the past, IoT devices were mainly operated at the remote end. After the user delivered the service demand task, a one-to-one device operation was possible through network communication. Owing to the changes in technology and demands today, the communication infrastructure has been transformed from a one-to-one approach to a oneto-many model, and even a many-to-many model has evolved. The operator no longer only performs a single operation on each device, so it is necessary to make the device itself gradually intelligent through the calculation of the communication design algorithm, which is a major technological breakthrough. In this study, we will show how to use data communication computing algorithms and communication systems to build intelligent architectures and proactively respond to services across different devices. In addition, using a wake-up detection system, it is possible to avoid the problem that the device responds after receiving the task and delays the delivery of the task result. Experimental results show the efficiency of the proposed method. The variance of real response time was limited within a small interval as the number of devices was increased.