PREACO: A fast ant colony optimization for codebook generation

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-based algorithms are essentially redundant and thus can be eliminated to boost their convergence speed, especially for large and complex problems. To evaluate the performance of the proposed method, we compare it with several state-of-the-art metaheuristic algorithms. Our simulation results indicate that the proposed method can significantly reduce the computation time of ACO-based algorithms evaluated in this paper while at the same time providing results that match or outperform those ACO by itself can provide.     

Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors

Dispatching rules are important to the performance of a manufacturing system. Selective applications of different priority rules at different processing stages in a multiple workstation manufacturing system have a positive impact on shop performance. This type of problem is a combinatorial dispatching decision. However, no dispatching rule can consistently produce better performance than all other rules under a variety of operating conditions and criteria. It is the purpose of this study to provide a robust solution for a dispatching decision that will have ‘good’ performance under different operating scenarios. In this paper a simulation case of a flow shop with multiple processors is proposed, specifically a multi-layer ceramic capacitor manufacturing system. Two multiple criteria decision-making methods – techniques for order preference by similarity to ideal solution (TOPSIS) and an analytic hierarchy process (AHP) – in combination with Taguchi orthogonal array are used to find the most suitable dispatching rule for every workstation. The results show that for 15 production scenarios and 4 criteria this combinatorial dispatching rule is robust, in the sense that it outperforms other commonly employed strategies.     

An efficient bit-based feature selection method

Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.     

Capillary HPLC-NMR Coupling: High-Resolution (1)H NMR Spectroscopy in the Nanoliter Scale

Coupling HPLC and NMR is one of the most powerful techniques for simultaneous separation and structural elucidation of unknown compounds in mixtures. To date, however, minimizing the detection volume, as is required when coupling NMR with miniaturized separation techniques, has been accompanied by a dramatic loss in resolution of the NMR spectra. Here, we report on the coupling of gradient capillary HPLC with on-column, high-resolution NMR detection. On-line stopped-flow and static (1)H NMR spectra were acquired with capillary columns of 75-315 μm i.d. With detection over a length of 1.2 cm, cell volumes cover a range of 50-900 nL. An on-line-detected NMR separation of dansylated amino acids was carried out in a 315 μm i.d. fused silica capillary packed to a length of 12 cm with C(18) stationary phase. The low solvent consumption makes the use of fully deuterated solvents economically feasible. NMR spectra with resolution on the order of 3 Hz were obtained using a 50 nL detection cell to measure 1.1 nmol of dansylated γ-aminobutyric acid under static conditions in a 75 μm i.d. capillary.     

Electric contacts inspection using machine vision

Machine vision is an excellent tool for inspecting a variety of industrial items such as textiles, printed circuit boards, electric components, labels, integrated circuits (IC), machine tools and fruits. In this paper, we propose machine vision-based inspection system for electric contact (EC), which are popularly used in switches, breakers and relays, and are important components in the electrical industry. The proposed system consists of three sub-systems, which inspect the top, side, and bottom surfaces of electric contact for different types of defects respectively. The system acquires the digital image of three views and classifies the surface defects including cracks, breaks, and scratches. For each view, this study develops different image pre-processing and feature extraction methods to enhance and detect the surface defects. The proposed system was implemented and verified using 229 samples collected from the EC production lines. Experimental results show the proposed system is effective and efficient in identifying EC defects.     

Effects of stray magnetic forces on position error signals during a long seeking-and-settling process

This paper is to study how stray magnetic forces encountered in a long seeking process affect position errors of a hard disk drive after it finishes the seek and settles. The study consists of three parts: analysis of stray magnetic forces, numerical modeling, and analysis of numerical results. In the analysis of stray magnetic forces, we lump the stray magnetic forces into three components D1, D2 and D4. Specifically, D1 is a pair of stray magnetic forces in the plane of the voice coil. The two forces act on the two equal legs of the voice coil. In addition, the two forces point to and away from the pivot center, respectively. D2 is a pair of stray magnetic forces out of the plane of the voice coil. The two forces are equal in magnitudes but opposite in directions. The two force components also act on the two equal legs of the voice coil. D4 is identical to D2, except that the two force components in D4 act in the same direction. In the numerical study, we adopt a numerical model that includes a spinning spindle motor, a spinning disk pack with multiple disks, a stationary base plate with a top cover, and a slewing head-stack assembly. Moreover, multiple bearings are present in the model to connect the multiple components. In particular, fluid-dynamic bearings connect the rotating spindle and disk pack with the base plate, pivot bearings connect the base plate with the head-stack assembly, and air bearings connect the spinning disk pack with head sliders located at the tip of the slewing head-stack assembly. Also, the numerical model assumes that the head-stack assembly seeks according to a user-specified seeking profile. Numerical simulations show two major conclusions. First, stray magnetic force component D1 does not lead to significant position errors when the head-stack assembly settles. Stray magnetic force components D2 and D4, however, can affect the position errors by significantly exciting torsion and bending modes of the head-stack assembly. Second, a flex cable can significantly increase position errors below 1 kHz during settling.     

Chattering reduction of sliding mode control by low‐pass filtering the control signal

The conventional approach to reducing control signal chattering in sliding mode control is to use the boundary layer design. However, when there is high‐level measurement noise, the boundary layer design becomes ineffective in chattering reduction. This paper, therefore, proposes a new design for chattering reduction by low‐pass filtering the control signal. The new design is non‐trivial since it requires estimation of the sliding variable via a disturbance estimator. The new sliding mode control has the same performance as the boundary layer design in noise‐free environments, and outperforms the boundary layer design in noisy environments. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Consideration factors and adoption of type,tabulation and framework for creating e-portfolios

The purpose of this research is to analyze the content of e-portfolios created by students in order to understand their tabulation and ways of displaying content. The analytic result shows that the number of outcome portfolios created by students is more than that of process portfolios. The five types of e-portfolio tabulation, in order of those most commonly created by students, are combination-based, content item-based, work-based, course unit-based, and time-based. The combination-based type incorporates the advantages of other tabulation types, while the content item-based and work-based types are better for clearly classifying data and step-by-step organization of it. Future research may further explore factors related to students’ decision of tabulation type, the difficulties they face in the process, and their mentality as they adopt a portfolio type.     

Boosted multi-task learning

In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between ? ₁-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.