Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge

Students learn new instructions well by building on relevant prior knowledge, as it affects how instructors and students interact with the learning materials. Moreover, studies have found that good prior knowledge can enable students to attain better learning motivation, comprehension, and performance. This suggests it is important to assist students in obtaining the relevant prior knowledge, as this can enable them to engage meaningfully with the learning materials. Tests are often used to help instructors assess students’ prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to each student, and this may mean that students are unable to realize their own individual weaknesses. To address this problem, instructors can diagnose the test results to provide more detailed information to each student, but this is obviously a time-consuming process. Therefore, this study proposes a testing-based diagnosis system to assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken. Furthermore, an experiment was conducted to evaluate the effectiveness of the proposed approach in an interdisciplinary course, since several studies have indicated that students learn more and better in such courses when applying relevant prior knowledge to what they are learning. The experimental results show that the developed system is able to effectively diagnose students’ prior knowledge and enhance their learning motivation and performance on an interdisciplinary course. In addition, two diagnostic evaluations were also conducted to assess whether the diagnoses given by the system were consistent with the decisions of experts. The results demonstrate that the proposed system can effectively assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken, since the diagnoses produced by the system were broadly consistent with those of experts.     

Solving single-product economic lot-sizing problem with non-increasing setup cost,constant capacity and convex inventory cost in O(N log N) time

This paper considers an economic lot sizing model with constant capacity, non-increasing setup cost, and convex inventory cost function. Algorithms with computational time of O(N×TD_N)have been developed for solving the model, where N is the number of planning periods and TD_N is the total demand. This study partially characterizes the optimal planning structure of the model. A new efficient algorithm with computational time of O(N log N) has also been developed based on the partial optimal structure. Moreover, computational study demonstrates that the new algorithm is efficient.     

Optimization of screen-printing parameters of SN9000 ink for pinholes using Taguchi method in chip on film packaging

The influence of pinholes on the chip on film (COF) in screen-printing was studied in this paper. Yield improvement is the most challenging part of COF manufacturing in view of its processing complexity, mainly in the screen-printing process. The process parameters such as ink capacity, origin control distance, angle of the squeezer, method of mixing, freshness of ink, speed of printing, and speed of scraper are considered to improve the pinholes. In Taguchi method, a two level orthogonal array is used to determine the signal-to-noise (S/N) ratio. Analysis of variance and the F-test values are used to determine the most significant process parameters affecting the pinholes. Confirmation analysis tests with the optimal levels of process parameters are carried out. The results of the experiment show that Taguchi method is a very suitable approach with regard to solving quality problems related to such pinholes.     

A two-phase dynamic dispatching approach to semiconductor wafer testing

There have been numerous advancements and rising competition in semiconductor technologies. In light of this, the wafer test plays a more significant role in providing the most prompt yield feedback for quick process improvement. However, the wafer test shop floor is getting more complicated than ever before because of the increasing change-over rate, nonlinear wafer arrival, and preemption by urgent orders. Furthermore, the foundry wafer test is a heterogeneous production with different production cycle times and a large variety of nonidentical testers. Shop floor conditions, including work in process (WIP) pool, tester status, and work order priority, continuously change. There is a need to operate the kind of production line that simultaneously fulfills multiple objectives. Such objectives are maximum confirmed line item performance (CLIP) for normal lots, 100% CLIP for urgent lots, minimum change-over rate, and shortest cycle time. Thus, a reactive dispatching approach is proposed and expected to perform a real-time solution no matter how/what the shop floor would change. The dynamic approach is mainly triggered by two kinds of major events: one is when an urgent lot comes in, and the other is when a tester is idle. In addition, through a two-phase dispatching algorithm, lot ranking, and lot assignment methods, prioritized WIP lots and an appropriate lot assignment are suggested. A better performance measure is obtained by considering the multiple objectives the wafer test operations seek to achieve.     

A two-stage feature selection method for text categorization

Feature selection for text categorization is a well-studied problem and its goal is to improve the effectiveness of categorization, or the efficiency of computation, or both. The system of text categorization based on traditional term-matching is used to represent the vector space model as a document; however, it needs a high dimensional space to represent the document, and does not take into account the semantic relationship between terms, which leads to a poor categorization accuracy. The latent semantic indexing method can overcome this problem by using statistically derived conceptual indices to replace the individual terms. With the purpose of improving the accuracy and efficiency of categorization, in this paper we propose a two-stage feature selection method. Firstly, we apply a novel feature selection method to reduce the dimension of terms; and then we construct a new semantic space, between terms, based on the latent semantic indexing method. Through some applications involving the spam database categorization, we find that our two-stage feature selection method performs better.     

Condition for relaxed Monte Carlo method of solving systems of linear equations

In this paper, we point out the limitation of the paper entitled “Solving Systems of Linear Equations with Relaxed Monte Carlo Method” published in this journal (Tan in J. Supercomput. 22:113–123, 2002). We argue that the relaxed Monte Carlo method presented in Sect. 7 of the paper is only correct under the condition that the coefficient matrix A must be diagonal dominate. However, for nondiagonal dominate case; the corresponding Neumann series may diverge, which would lead to infinite loop when simulating the iterative Monte Carlo algorithm. In this paper, we first prove that only for the diagonal dominate matrix, the corresponding von Neumann series can converge, and the Monte Carlo algorithm can be relaxed. Therefore, it is not true for nondiagonal dominate matrix, no matter the relaxed parameter γ is a single value or a set of values. We then present and analyze the numerical experiment results to verify our arguments.     

A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners?? preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners?? requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.     

New Compliance-Mechanism Design for Small Companion Robots

The compliance mechanisms used on robotic arms can be classified into two major categories: mechanical and electronic. The ideal characteristics of a compliance mechanism include small volume, simple mechanical structure, low cost, large complaint range, and high precision and accuracy under displacement control. Most mechanical compliance mechanisms are able to meet the first three conditions but have a small compliant range and low precision and accuracy under displacement control. The electronic compliance mechanism is hardly limited in the degree of deformation and comes with a higher precision and accuracy under the displacement control, but its sensors are expensive and the system is difficult to control. To combine the advantages of both types, this research aims to develop a new design of compliance mechanism in which a small-scale torque-limiting mechanism with a self-locking feature is installed between the actuator and the arm structure to minimize the volume while providing an ample torque limit. When the robotic arm is overloaded under an external force, a slide will occur inside the compliance mechanism so that the robotic arm will move along the direction of the external force to avoid damage. The robotic arm will automatically return to its original position after the external force is removed. The new compliance mechanism not only exceeds most of the current mechanical designs in the range of compliance but also does not affect the precision and accuracy of the displacement control. Furthermore, the new compliance mechanism does not require any sensors, which will benefit small robotic arms.     

Efficient detection and tracking of moving objects in geo-coordinates

We present a system to detect and track moving objects from an airborne platform. Given a global map, such as a satellite image, our approach can locate and track the targets in geo-coordinates, namely longitude and latitude obtained from geo-registration. A motion model in geo-coordinates is more physically meaningful than the one in image coordinates. We propose to use a two-step geo-registration approach to stitch images acquired by satellite and UAV cameras. Mutual information is used to find correspondences between these two very different modalities. After motion segmentation and geo-registration, tracking is performed in a hierarchical manner: at the temporally local level, moving image blobs extracted by motion segmentation are associated into tracklets; at the global level, tracklets are linked by their appearance and spatio-temporal consistency on the global map. To achieve efficient time performance, graphics processing unit techniques are applied in the geo-registration and motion detection modules, which are the bottleneck of the whole system. Experiments show that our method can efficiently deal with long term occlusion and segmented tracks even when targets fall out the field of view.