• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Journal of Plant Biotechnology
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• v.34 no.3
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• pp.201-205
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• 2007

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts of higher plants is applied to discriminate plants genetically, leaf samples of eight cultivars of Catharanthus roseus were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR fingerprint region data were analyzed by principal component analysis (PCA). Major peaks as biomarkers were identified as the most significant contributors to distinguish samples by using genetic programming. A hierarchical dendrogram based on the results from PCA separated the eight cultivars into two major groups in the same manner as the dendrograms based on genetic fingerprinting methods such as RAPD and AFLP. A slight difference between the dendrograms was found only in branching pattern within each subgroup. Therefore, we conclude that the hierarchical dendrogram based on PCA of the FT-IR data represents the most probable chemotaxonomical relationship between cultivars, which is in general agreement with the genetic relationship determined by conventional DNA fingerprinting methods.

• Journal of Plant Biology
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• v.2 no.1
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• pp.35-42
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• 1959

The cuttings and apical portion of stein in sweet potato were growlh under artificial light and specific photoperied and temperature. Though the plant growth was poor under insufficient light intensity of artificial light, the tuber formations were induced at long light period (16L+8D) and not induced at short light period (8L+16D) and low temperature of darkparied. The determinative factor for tuber formation of sweet potato seems to be a stimulation which has intimate relationship with specific photoperiod and temperature and no direct relation with the growth of plant body and light intensity. The root pattern of inductive state for tubers and non-inductive state were different distinctly, the former were silky and slender, the latter were branchy, and stout appearence. This different root pattern must be due also to the specific photoperiod and temperature and may have any relationship with the stimulation for tuber formation from the point of auxin physiology etc.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.520-537
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• 2019

This paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.

• The korean journal of orthodontics
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• v.37 no.3 s.122
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• pp.192-203
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• 2007

Objective: The aim of this study was assessment of the relationship between airway space and facial morphology in Class III children with nasal obstruction. Methods: For this study, 100 Class III children (50 boys and 50 girls) were chosen. All subjects were refered to ENT, due to nasal obstruction. Airway space measurements and facial morphology measurements were measured on lateral cephalometric radiograph. Pearson correlation analysis was used to assess the relationship between airway space and facial morphology Results: Ramal height, SNA, SNB, PFH, FHR and facial plane angle were positively related to upper PAS, and sum of saddle angle, articular angle, and genial angle, SN-GoGn, Y-axis to SN and FMA negatively related to upper PAS. Genial angle, FMA were positively related to lower PAS, and articular angle, facial depth, PFH and FHR negatively related to lower PAS. PCBL, ramal height, Mn. body length, Mn. body length to ACBL, facial depth, facial length, PFH and AFH were positively related to tonsil size. Sum of saddle angle, articular angle, genial angle, facial length, AFH, FMA and LFH were positively related to tongue gap, and IMPA and overbite was negatively related to tongue gap. Upper PAS, related to size of adenoid tissue, was mainly related to posterior facial dimension following a vertical growth pattern of face and mandibular rotation. Lower PAS and tonsil size, related to anterior-posterior tongue base position, were significantly related to each other. Lower PAS was related to growth pattern of mandible, and tonsil size was related to size of mandible and horizontal growth pattern of face. Tongue gap was related to anterior facial dimension following a vertical growth pattern of face. Conclusion: Significant relationship exists between airway space and facial morphology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.4
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• pp.1872-1879
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• 2012

By analyzing the use of outdoor space in apartment complex and finding empirical proof which is concrete and systemic, this study focuses on the relationship between residents' daily lives and physical space. This study tries to find out the types and characteristics of outdoor space which causes the occurrence of residents' use in High-Rise apartment housing Estates, and analyzes the residents' usage pattern of outdoor space in eight different apartment complexes which were builted since 2004. After analyzing the usage pattern of outdoor space, the usage of playground was ranked highest and playing, talking, exercising, resting was in order. Especially it was found that the usage inside semi-public space where passing is abundant increases.

• Textile Coloration and Finishing
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• v.17 no.5 s.84
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• pp.45-52
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• 2005

The optimum fabric design plan is recently needed with the relevant fabric shrinkage in dyeing and finishing processes for the various sensible fabrics for garment. For responding this need, the difference of fabric design plan such as fabric density, yarn count and finishing shrinkage has to be surveyed with weaving looms such as water jet, air-jet and rapier looms, and also has to be analysed with weave pattern such as plain, twill and satin according to the fabric manufacturers. Therefore, in this study, 420 polyester weave pattern sheets are collected from 5 fabric manufacturers, and the weave density coefficients of these fabrics are calculated using yarn count, fabric density and 1 repeat weave pattern. These weave density coefficients are analysed according to the fabric manufacturers, weaving looms and various weave patterns. And also the weave density coefficients of 420 polyester fabrics are analysed with finishing shrinkage according to the fabric manufacturers and various weave patterns such as plain, twill and satin fabrics.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.73-78
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• 2004

By modeling the block matching algorithm as a function of the correlation of image blocks, we derive search patterns for fast block matching motion estimation. The proposed approach provides an analytical support lot the diamond-shape search pattern, which is widely used in fast block matching algorithms. We also propose a new fast motion estimation algorithm using adaptive search patterns and statistical properties of the object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the block differences. By changing the search pattern adaptively, we improve motion prediction accuracy while reducing required computational complexity compared to other fast block matching algorithms.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.6
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• pp.1-6
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• 2010

Patterned optical components are widely used for optical products such as LCD and lighting. Since CCFL was used as a light source in the products, prism films having linear continuous optical patterns were widely used. However, LED which is a dot light source is popular recently, therefore, the optical products need new optical components having non-continuous optical patterns. Indentation machining method is a powerful method for machining of non-continuous pattern. When a copper mold and a brass mold were machined by this method, severe plastic deformation called pile-up was observed around the patterns. Since pile-up has negative relationship to ductility, this deformation can be eliminated by annealing treatment which makes the materials ductile. No plastic deformation occurred when machined after annealing at $600{^{\circ}C}$ and $575{^{\circ}C}$ for copper and brass, respectively. Finally, non-continuous optical patterns with high quality were machined on a copper mold and a brass mold successively.