The paper-and-pencil programming strategy (PPS) is a way of representing an idea logically by any representation that can be created using paper and pencil. It was developed for non-computer majors to improve their understanding and use of computational thinking and increase interest in learning computer science. A total of 110 non-majors in their sophomore year were assigned to either a Logo or a PPS course with attendance being 2 hours per week for 15 weeks. To measure the effectiveness of PPS, the Group Assessment of Logical Thinking and a self-assessment survey pre- and post-test were used. Findings indicated that PPS not only improved students' overall logical thinking as much as did Logo programming learning, but also increased scores on one more subscale of logical thinking than did the Logo course. In addition, PPS significantly helped students understand the concept of computational thinking and increased their interest in learning computer science.
This study solved the problem of unstable production chains by considering allocation rate conformance. We proposed two phased algorithm suitable for solving production planning that considers allocation rate conformance; the first phase was heuristic initial solution generation, and the second phase was tabu-search based solution improvement. By using three data sets which have different sizes of data and three different criteria, the results of proposed algorithm were compared with MIP results. The proposed algorithm showed the best production plan in terms of allocation rate conformance, and it was appropriate for other criteria; it solved the problem of unstable production chains by solving concentrated and unfair allocation.
In this paper, we propose a new multi-path routing protocol to provide reliable and stable data transmission in MANET that is composed of high-mobility nodes. The new multi-path routing establishes the main route by the mechanism based on AODV, and then finds the backup route that node-disjoint from the main route by making add nodes in the main route not participate in it. The data transmission starts immediately after finding the main route. And the backup route search process is taking place while data is transmitted to reduce the transmission delay. When either of the main route or the backup route is broken, data is transmitted continuously through the other route and the broken route is recovered to node-disjoint route by the route maintenance process. The result of the simulation based on the Qualnet simulator shows that the backup route exists 62.5% of the time when the main route is broken. And proposed routing protocol improved the packet transmission rate by 2~3% and reduced the end-to-end delay by 10% compared with AODV and AODV-Local Repair.
Smart TV is expected to bring cloud services based on virtualization technologies to the home environment with hardware and software support. Although most physical resources can be shared among virtual machines (VMs) using a time sharing approach, allocating the proper amount of memory to VMs is still challenging. In this paper, we propose a novel mechanism to dynamically balance the memory allocation among VMs in virtualized Smart TV systems. In contrast to previous studies, where a virtual machine monitor (VMM) is solely responsible for estimating the working set size, our mechanism is symbiotic. Each VM periodically reports its memory usage pattern to the VMM. The VMM then predicts the future memory demand of each VM and rebalances the memory allocation among the VMs when necessary. Experimental results show that our mechanism improves performance by up to 18.28 times and reduces expensive memory swapping by up to 99.73% with negligible overheads (0.05% on average).
The open platform communications unified architecture (OPC UA) is a major industry-standard middleware based on the request–reply pattern, and the data distribution service (DDS) is an industry standard in the publish–subscribe form. The OPC UA cannot replace fieldbuses at the control and field levels. To facilitate real-time connectionless operation, the OPC Foundation added the publish–subscribe model—a new specification that supports broker functions, such as message queuing telemetry transport (MQTT), and advanced message queuing protocol (AMQP)—to the OPC UA Part 14 standard. This paper proposes a protocol converter for incorporation into the application layer of the DDS subscriber to facilitate interoperability among publisher–subscriber pairs. The proposed converter comprises a DDS gateway and bridge. The former exists inside the MQTT and AMQP brokers, which convert OPC UA publisher data into DDS messages prior to passing them on to the DDS subscriber. The DDS bridge passes the messages received from the DDS gateway to the OPC UA subscriber in the corresponding DDS application layer. The results reported in existing studies, and those obtained using the proposed converter, allow all devices supporting the OPC UA and OPC UA PubSub standards to realize DDS publish–subscribe interoperability.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.
Abstract This paper proposes a supervised learning with a class-balancing loss function (SL-CBL) approach for fault detection and feature-similarity-based recipe optimization (FSRO) for a plastic injection molding process. SL-CBL is a novel method that can accurately classify an input sample as a normal or fault condition, even when the training data are severely class-imbalanced. The proposed class-balancing loss function consists of the weighted focal loss and the loss of the F1 score; together, these are used to correctly classify even a small number of faulty samples. SL-CBL is investigated with four classifiers of different structures; the classifiers consist of several fully connected and batch normalization layers. FSRO is an optimization scheme that finds the optimal recipe whose feature is similar to the features of normal samples. The optimal solution is obtained by minimizing the Euclidean distance to the centroid of the normal features. In this research, the proposed SL-CBL and FSRO methods are validated by applying them to an industrial plastic injection molding dataset. The validation results show that the proposed SL-CBL approach achieves the highest F1 score with the lowest misclassification rate, as compared to the alternative methods. When visualizing the feature space, the optimal recipe found by the FSRO scheme was found to be close to the centroid of the normal features, even if the initial recipe is classified as a fault. Furthermore, each variable of the optimized recipe lies within the confidence interval of 3${\rm{\sigma }}$ for the normal condition. This indicates that the optimal recipe is statistically similar to the normal samples.
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