• Journal of the Korean Physical Society
• /
• v.73 no.11
• /
• pp.1644-1649
• /
• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Journal of Software Assessment and Valuation
• /
• v.17 no.2
• /
• pp.1-10
• /
• 2021

The ability to recognize human emotions by computer vision is a very important task, with many potential applications. Therefore the demand for emotion recognition using not only RGB images but also thermal images is increasing. Compared to RGB images, thermal images has the advantage of being less affected by lighting conditions but require a more sophisticated recognition method with low-resolution sources. In this paper, we propose a Divide and Conquer-based CNN training strategy to improve the performance of facial thermal image-based emotion recognition. The proposed method first trains to classify difficult-to-classify similar emotion classes into the same class group by confusion matrix analysis and then divides and solves the problem so that the emotion group classified into the same class group is recognized again as actual emotions. In experiments, the proposed method has improved accuracy in all the tests than when recognizing all the presented emotions with a single CNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.665-666
• /
• 2022

Differential evolution is an efficient algorithm for continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems quickly degrades performance and exponentially increases runtime. To overcome this problem, a new cooperative coevolution differential evolution based on Spark (referred to as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.559-560
• /
• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems dramatically degrades performance and exponentially increases runtime. Therefore, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.6
• /
• pp.322-329
• /
• 2000

BIST(Built-in Self Test) has been considered as the most promising DFT(design-for-test) scheme for the present and future test strategy. The most serious problem in applying BIST(Built-in Self Test) into a large circuit is the excessive increase in test time. This paper is focused on this problem. We proposed a new BIST construction scheme which uses a parallel divide-and-conquer method. The circuit division is performed with respect to some internal nodes called test points. The test points are selected by considering the nodal connectivity of the circuit rather than the testability of each node. The test patterns are generated by only one linear feedback shift register(LFSR) and they are shared by all the divided circuits. Thus, the test for each divided circuit is performed in parallel. Test responses are collected from the test point as well as the primary outputs. Even though the divide-and-conquer scheme is used and test patterns are generated in one LFSR, the proposed scheme does not lose its pseudo-exhaustive property. We proposed a selection procedure to find the test points and it was implemented with C/C++ language. Several example circuits were applied to this procedure and the results showed that test time was reduced upto 1/2151 but the increase in the hardware overhead or the delay increase was not much high. Because the proposed scheme showed a tendency that the increasing rates in hardware overhead and delay overhead were less than that in test time reduction as the size of circuit increases, it is expected to be used efficiently for large circuits as VLSI and ULSI.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.11
• /
• pp.4502-4518
• /
• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• Journal of information and communication convergence engineering
• /
• v.19 no.3
• /
• pp.155-160
• /
• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• BMB Reports
• /
• v.52 no.4
• /
• pp.239-249
• /
• 2019

Membrane-anchored full-length MET stimulated by its ligand HGF/SF induces various biological responses, including survival, growth, and invasion. This panel of responses, referred to invasive growth, is required for embryogenesis and tissue regeneration in adults. On the contrary, MET deregulation is associated with tumorigenesis in many kinds of cancer. In addition to its well-documented ligand-stimulated downstream signaling, the receptor can be cleaved by proteases such as secretases, caspases, and calpains. These cleavages are involved either in MET receptor inactivation or, more interestingly, in generating active fragments that can modify cell fate. For instance, MET fragments can promote cell death or invasion. Given a large number of proteases capable of cleaving MET, this receptor appears as a prototype of proteolytic-cleavage-regulated receptor tyrosine kinase. In this review, we describe and discuss the mechanisms and consequences, both physiological and pathological, of MET proteolytic cleavages.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.4
• /
• pp.409-415
• /
• 2003

Recently, the modular learning are very popular and receive much attention for pattern classification. The modular learning method based on the "divide and conquer" strategy can not only solve the complex problems, but also reach a better result than a single classifier′s on the learning quality and speed. In the neural network area, some researches that take the modular learning approach also have been made to improve classification performance. In this paper, we propose a simple modular neural network for characters recognition of vehicle number plate and evaluate its performance on the clustering methods of feature vectors used in constructing subnetworks. We implement two clustering method, one is grouping similar feature vectors by K-means clustering algorithm, the other grouping unsimilar feature vectors by our proposed algorithm. The experiment result shows that our algorithm achieves much better performance.

• The Journal of Korea Robotics Society
• /
• v.2 no.4
• /
• pp.304-309
• /
• 2007

This paper describes an efficient path generation method for area coverage. Its applications include robots for de-mining, cleaning, painting, and so on. Our method is basically based on a divide and conquer strategy. We developed a novel cell decomposition algorithm that divides a given area into several cells. Each cell is covered by a robot motion that requires minimum time to cover the cell. Using this method, completeness and time efficiency of coverage are easily achieved. For the completeness of coverage in dynamic environments, we also propose a path following method that makes the robot cover missed areas as a result of the presence of unknown obstacles. The effectiveness of the method is verified using computer simulations.