Language supports for journal abstract writing across disciplines

Various writing assistance tools have been developed through efforts in the areas of natural language processing with different degrees of success of curriculum integration depending on their functional rigor and pedagogical designs. In this paper, we developed a system, WriteAhead, that provides six types of suggestions when non‐native graduate students of English from different disciplines are composing journal abstracts, and assessed its effectiveness. The method involved automatically building domain‐specific corpora of abstracts from the Web via domain names and related keywords as query expansions, and automatically extracting vocabulary and n‐grams from the corpora in order to offer writing suggestions. At runtime, learners' input in the writing area of the system actively triggered a set of corresponding writing suggestions. This abstract writing assistant system facilitates interactions between learners and the system for writing abstracts in an effective and contextualized way, by providing suggestions such as collocations or transitional words. For assessment of WriteAhead, we compared the writing performance of two groups of students with or without using the system, and adopted student perception data. Findings show that the experiment group wrote better, and most students were satisfied with the system concerning most suggestion types, as they can effectively compose quality abstracts through provided language supports from WriteAhead.     

Hybrid recommendations for mobile commerce based on mobile phone features

Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one‐to‐one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so‐called sparsity problem, which means there may not be sufficient similar users because the user‐item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)‐based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF‐based method and a preference‐based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.     

DYNAMIC ANALYSES AND ROBUST STEERING CONTROLLER DESIGN FOR AUTOMATED LANE GUIDANCE OF HEAVY‐DUTY VEHICLES

In this paper, we present various linear analyses of the linearized lateral dynamics of heavy‐duty vehicles (HDVs) (tractor‐semitrailer type), which include time domain, frequency domain and pole/zero analyses. These analyses are conducted to examine the vehicle response to the steering input subjected to variations of speed, road adhesion coefficient, cargo load in the trailer, and look‐ahead distance for the lateral deviation sensor. These parameters (uncertainties) have significant influence on vehicle dynamics. It has been shown that redefining the look‐ahead lateral error as the controlled output has a favorable impact on the lateral control problem. Based on these analyses, a robust steering controller using H_∞ loop‐shaping procedure is designed for a tractor semitrailer combination to follow the road center line on both curved and straight highway sections. The proposed controller ensures the robust performance under model uncertainties which include varying vehicle longitudinal speed, road adhesion coefficient, and cargo load in the trailer. The performance of the designed controller is evaluated by simulations and validated by experiments.     

A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks.     

MINIMAL VALID AUTOMATA OF SAMPLE SEQUENCES FOR DISCRETE EVENT SYSTEMS

Minimal valid automata (MVA) refer to valid automata models that fit a given input‐output sequence sample from a Mealy machine model. They are minimal in the sense that the number of states in these automata is minimal. Critical to system identification problems of discrete event systems, MVA can be considered as a special case of the minimization problem for incompletely specified sequential machine (ISSM). While the minimization of ISSM in general is an NP‐complete problem, various approaches have been proposed to alleviate computational requirement by taking special structural properties of the ISSM at hand. In essence, MVA is to find the minimal realization of an ISSM where each state only has one subsequent state transition defined. This paper presents an algorithm that divides the minimization process into two phases: first to give a reduced machine for the equivalent sequential machine, and then to minimize the reduced machine into minimal realization solutions. An example with comprehensive coverage on how the associated minimal valid automata are derived is also included.     

Surface treatment,reflectance, and age effects on electronic‐paper reading performance

Abstract— This study is intended to explore the legibility and visual fatigue of different age users under various surface treatments and reflectance of electronic paper. Through the method of character‐search task, the results indicated that compared with single types of treatment [anti‐reflection (AR) 0.8%, anti‐glare, 43% haze), the compound treatment of anti‐reflection and anti‐glare (ARC) exhibited the same legibility, and it showed superior properties to effectively reduce visual fatigue. Hence, it is suggested that electronic‐paper manufacturers should choose the compound surface treatment for better visual performance. On the other hand, the findings also validated that enhancing the reflectance of electronic paper to the same level as regular paper (about 80%) is worthy to be practically implemented. Based on the results of this study, electronic‐paper manufacturers can take useful information to fulfill ergonomic requirements on product design.     

Rectification of legibility distance in a driving simulator

Visual differences lead to differences in the legibility distances of traffic signs between driving simulators and real road environments. To ensure that the legibility distance in a simulator is similar to that in the real world, this study proposes a theoretical equation for predicting legibility distance and a simple algorithm for determining the magnifying power of a traffic sign for a display system in a simulator. Experiments of traffic sign recognition using a simulator were conducted under quasi-static and dynamic driving conditions. On-road tests were also carried out under quasi-static and dynamic driving conditions. Thirty healthy and non-disabled volunteers were recruited. The experimental results showed that the proposed theoretical equation for predicting legibility distance and the simple algorithm for determining the magnifying power of traffic signs reduced the difference in legibility distances between the simulator and real road environment under quasi-static and dynamic driving conditions.     

Wafer defect inspection by neural analysis of region features

Wafer defect inspection is an important process that is performed before die packaging. Conventional wafer inspections are usually performed using human visual judgment. A large number of people visually inspect wafers and hand-mark the defective regions. This requires considerable personnel resources and misjudgment may be introduced due to human fatigue. In order to overcome these shortcomings, this study develops an automatic inspection system that can recognize defective LED dies. An artificial neural network is adopted in the inspection. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in the experiments. The results show that the proposed approach successfully identified the defective dies on LED wafers. Personnel costs and misjudgment due to human fatigue can be reduced using the proposed approach.     

An expanded SEMATECH CIM framework for heterogeneous applications integration

A significant effort in recent computer-integrated manufacturing (CIM) development was carried out by SEMATECH who had laid out and created an open framework for the integration of manufacturing execution system applications running in semiconductor industries at the factory operation level. This paper presents the feasibility to incorporate basic manufacturing applications at the factory engineering level, which include product design, process planning, and material requirement planning, into the SEMATECH CIM framework to form a generic framework across both factory operation level and factory engineering level. The CIM framework established in this paper was aimed to provide a reusable integrated system framework that clearly specifies the functional interface boundaries and standard information model of the required components, in general, manufacturing systems at both factory operation and factory engineering levels. Standard unified modeling language (UML) diagrams and Petri nets have been utilized to model and analyze the specifications and dynamic behaviors of this generic CIM framework. The goal is to build a framework by creating a common, modular, flexible, and integrated object model that unifies an advanced object-oriented architecture concept and heterogeneous manufacturing application development in an open and multisupplier CIM system environment.     

Fast agglomerative clustering using information of k-nearest neighbors

In this paper, we develop a method to lower the computational complexity of pairwise nearest neighbor (PNN) algorithm. Our approach determines a set of candidate clusters being updated after each cluster merge. If the updating process is required for some of these clusters, k-nearest neighbors are found for them. The number of distance calculations for our method is O(N²), where N is the number of data points. To further reduce the computational complexity of the proposed algorithm, some available fast search approaches are used. Compared to available approaches, our proposed algorithm can reduce the computing time and number of distance calculations significantly. Compared to FPNN, our method can reduce the computing time by a factor of about 26.8 for the data set from a real image. Compared with PMLFPNN, our approach can reduce the computing time by a factor of about 3.8 for the same data set.