Defects in multistage manufacturing processes (MMPs) decrease profitability and product quality. Therefore, MMP parameter optimization within a range is essential to prevent defects, achieve dynamic accuracy, and accommodate manufacturing tolerances. However, existing studies only focused on optimization in a single manufacturing stage of MMP, such as the weaving stage in fabric manufacturing. Furthermore, existing methods optimize for a single value rather than a range. Thus, we propose a novel approach called multistage parameter optimization for rule generation (MPORG) to prevent the occurrence of defects in MMPs. In the proposed approach, key parameters are identified and optimized to a range for each defect type. Subsequently, the optimized parameters for each defect type are merged. Our approach is novel because it optimizes parameters to a range rather than a single value, allowing engineers to select a value in this range according to their experience. It also provides results that are specific to a product type. Our approach outperformed the classification and regression tree (CART) algorithm and multiresponse CART method in experiments on an empirical fabric manufacturing dataset that we gathered. The experimental results demonstrated that the MPORG approach can prevent the occurrence of single-type or multiple-type defects by approximately 89%.

In batch production systems, detecting low-yield machines is essential for minimizing the production of defective pieces, which is a complex problem that currently requires multiple experts, considerable capital, or a combination of both to overcome. To solve this problem, we proposed a cost-efficient and straightforward method that involves using maximum likelihood estimation and bootstrap confidence intervals to estimate per-machine yield; this method enables identification of low-yield machines and generation of a list of these machines. Manufacturing engineers can use the list to perform necessary verification and maintenance processes. Before implementing this method, a manufacturer with 50–500 machines should build a dataset containing approximately 6–20 times as many batches as there are production machines. When this condition is met, the proposed method can be used effectively to detect up to five low-yield machines.

Fisher's linear discriminant analysis (LDA) is an easy-to-use supervised dimensionality reduction method. However, LDA may be ineffective against complicated class distributions. It is well-known that deep feedforward neural networks with rectified linear units as activation functions can map many input neighborhoods to similar outputs by a succession of space-folding operations. This short paper shows that the space-folding operation can reveal to LDA classification information in the subspace where LDA cannot find any. A composition of LDA with the space-folding operation can find classification information more than LDA can do. End-to-end fine-tuning can improve that composition further. Experimental results on artificial and open data sets have shown the feasibility of the proposed approach.

In this paper, a system for tactic analysis of fencing video is developed. The proposed system uses three novel views for presenting the tactic and critical distance of fencers in a match point-by-point. Since fencers fight each other directly, identifying the moment of launching an attack is crucial for analyzing fencing tactics. A criterion function in terms of the speed and acceleration of a fencer and the tempo of the fencer's footwork is proposed for identifying that moment. In addition, a rule-based approach based on the fencer speed and distance is proposed for recognizing the offensive tactic used to score a hit. Three experiments on the video clips for finals and semi-finals of individual events of the World Fencing Championships in 2013-2015 were conducted for evaluating the proposed system. The experiment also included objective and subjective assessments of twelve fencers. The experimental results have demonstrated the feasibility of the proposed approach.

Batch-mode active learning algorithms can select a batch of valuable unlabeled samples to manually annotate for reducing the total cost of labeling every unlabeled sample. To facilitate selection of valuable unlabeled samples, many batch-mode active learning algorithms map samples to the reproducing kernel Hilbert space induced by a radial-basis function (RBF) kernel. Setting a proper value to the parameter for the RBF kernel is crucial for such batch-mode active learning algorithms. In this paper, for automatic tuning of the kernel parameter, a hypothesis-margin-based criterion function is proposed. Three frameworks are also developed to incorporate the function of automatic tuning of the kernel parameter with existing batch-model active learning algorithms. In the proposed frameworks, the kernel parameter can be tuned in a single stage or in multiple stages. Tuning the kernel parameter in a single stage aims for the kernel parameter to be suitable for selecting the specified number of unlabeled samples. When the kernel parameter is tuned in multiple stages, the incorporated active learning algorithm can be enforced to make coarse-to-fine evaluations of the importance of unlabeled samples. The proposed framework can also improve the scalability of existing batch-mode active learning algorithms satisfying a decomposition property. Experimental results on data sets comprising hundreds to hundreds of thousands of samples have shown the feasibility of the proposed framework.

Today, vehicles have been an essential part of our daily life. According to survey, one-third drivers admit they have left their vehicle while it running, which makes the vehicle an easy target for theft. In this paper, we propose a novel driver identification method with wearable device. This approach can be used in continuous authentication to offer a greater degree of multilevel protection. Our proposed method is based on the hypothesis that a driver has a specific habit to drive a vehicle; and such behavioral biometrics can be captured from the wearable device. In order to validating this hypothesis, we have used the wireless sensor modules to collect driver's behavioral information. Then, the experimental results indicate that the proposed approach is feasible.

Today, vehicles have been an essential part of our daily life. Existing vehicle security systems rely primarily on lock devices, alarm systems or fingerprints as forms of protection. A non-intrusive driver authentication mechanism could be incorporated into existing vehicle security system to offer a greater degree of multilevel protection. In this paper, we propose a novel non-intrusive authentication mechanism based on dynamics of driver's upper body joint angles. This new approach is based on the hypothesis that a driver has a specific habit to drive a vehicle; and such behavioral biometrics can be captured from the motion capture system. We design an authentication mechanism that adopted 40 new features transformed from the information of the driver's upper body joints. To validate this hypothesis, we have used iPi motion capture system to collect driver's behavioral biometrics. The experimental results show that the proposed approach has an equal error rate about 13%.

In this paper, we present an indexing structure of data-driven cuboid patterns to speed up the process of liver detection and segmentation from computed tomography (CT) scans using the cube-based generalized Hough transform (CGHT). Most existing approaches to automatic liver segmentation from CT scans use a statistical shape model (SSM) integrated with a searching algorithm to recover the deformation. However, establishing the correspondences among landmark points of training shapes for the construction of the average shape of SSM remains as a challenge due to the high variation of liver shapes in CT scans. The proposed method is a fully automatic segmentation method that combines four steps. Firstly, a test CT volume is partitioned into multiple non-overlapped sub-volumes with each of them consisting of variable numbers of consecutive slices. Secondly, we locate the cube-based liver shapes in all sub-volumes via Hough voting and dynamic programming. Thirdly, we construct the basic 3D model through a level-set framework for liver shape segmentation. Finally, to introduce neighbors statistical analysis into the above model, we deform the 3D liver shape to overcome disturbances caused by noise and inhomogeneity. The MICCAI 2007 liver segmentation challenge datasets are used to verify the effectiveness of the proposed method. Experimental results demonstrate the good performance of the proposed method in terms of segmentation accuracy and execution speed.

In this study, we propose to adopt histogram features obtained from smartphone sensors for building authentication models, which could be used to nonintrusively authenticate smartphone users in varying operating scenarios (e.g. standing and sitting) when engaged in using stationary apps. We adopted two smartphone sensors, namely touchscreen and orientation sensor, to evaluate their feasibility. Consequently, sixteen touch-based features and thirty-three orientation-based features were separately used to construct two authentication models. To evaluate the performance of two constructed models, thirty-five subjects joined for collecting experimental data in two operating scenarios, standing and sitting. The experimental results showed that the equal error rate (EER) of touch-based model was approximately 6.56% with features extracted from ten flick touch gestures and reduced to approximately 3.05% with sixty flick touch gestures. For orientation-based model, the EERs were approximately 10.27% and 7.07%, separately. The results showed that the histogram features of the adopted two sensors are feasible for authentication purpose. Specially, this study further discusses the phenomenon of multiple behavioral pattern over the adopted two sensors caused among different operating scenarios, such as standing and sitting.

In this paper, the self organization properties of ant colonies are employed to tackle the problem of DNA copy number analysis in array CGH data, which can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions may be crucial events in the development and progression of cancer and other diseases. Accurately identifying the recurrent aberration at a particular genome location is important to find the possibly damaged genes. Unfortunately, it is difficult to exactly detect the boundaries of aberration from array CGH data with low signal-to-noise ratio. The presented ant colony optimization algorithm represents the problem as a directed graph such that the objective of the original problem becomes to find the shortest path on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The important properties of the proposed method are thoroughly investigated. The performance of the proposed method as compared to those of the state-of-the-art methods is very promising.

In this paper, a new kernel-based deformable model is proposed for detecting deformable shapes. To incorporate valuable information for shape detection, such as edge orientations into the shape representation, a novel scheme based on kernel methods has been utilized. The variation model of a deformable shape is established by a set of training samples of the shape represented in a kernel feature space. The proposed deformable model consists of two parts: a set of basis vectors describing the sample subspace, including the shape representations of the training samples, and a feasibility constraint generated by the one-class support vector machine to describe the feasible region of the training samples in the sample subspace. The aim of the proposed feasibility constraint is to avoid finding some invalid shapes. By using the proposed deformable model, an efficient algorithm without initial solutions is developed for shape detection. The proposed approach was tested against real images. Experimental results show the effectiveness of the proposed deformable model and prove the feasibility of the proposed approach.

A vision-based system for tracking and interpreting leg motion in image sequences using a single camera is developed for a user to control his movement in the virtual world by his legs. Twelve control commands are defined. The trajectories of the color marks placed on the shoes of the user are used to determine the types of leg movement by a first-order Markov process. Then, the types of leg movement are encoded symbolically as input to Mealy machines to recognize the control command associated with a sequence of leg movements. The proposed system is implemented on a commercial PC without any special hardware. Because the transition functions of Mealy machines are deterministic, the implementation of the proposed system is simple and the response time of the system is short. Experimental results with a 14-Hz frame rate on 320/spl times/240 image resolution are included to prove the feasibility of the proposed approach.

To develop a reliable computer vision system, the employed algorithm must guarantee good output quality. In this study, to ensure the quality of the pose estimated from line features, two simple test functions based on statistical hypothesis testing are defined. First, an error function based on the relation between the line features and some quality thresholds is defined. By using the first test function defined by a lower bound of the error function, poor input can be detected before estimating the pose. After pose estimation, the second test function can be used to decide if the estimated result is sufficiently accurate. Experimental results show that the first test function can detect input with low qualities or erroneous line correspondences and that the overall proposed method yields reliable estimated results.