• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.91-98
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• 2011

This paper proposes an efficient and accurate vision based recognition and tracking framework for texture free objects. We approached this problem with a two phased algorithm: detection phase and tracking phase. In the detection phase, the algorithm extracts shape context descriptors that used for classifying objects into predetermined interesting targets. Later on, the matching result is further refined by a minimization technique. In the tracking phase, we resorted to meanshift tracking algorithm based on Bhattacharyya coefficient measurement. In summary, the contributions of our methods for the underwater robot vision are four folds: 1) Our method can deal with camera motion and scale changes of objects in underwater environment; 2) It is inexpensive vision based recognition algorithm; 3) The advantage of shape based method compared to a distinct feature point based method (SIFT) in the underwater environment with possible turbidity variation; 4) We made a quantitative comparison of our method with a few other well-known methods. The result is quite promising for the map based underwater SLAM task which is the goal of our research.

• Fashion & Textile Research Journal
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• v.16 no.4
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• pp.645-652
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• 2014

This study identifies consumer needs and a pattern sensory evaluation of jacquard fabrics for raincoats using quick-drying-absorbing polyester. We investigate the consumer's consciousness and raincoat improvements. Twelve kinds of jacquard fabrics were developed for use in this study. Developed jacquard fabrics were assessed subjectively by 152 university students using a 7-point scale of 26 consumer needs and 31 pattern image sensory descriptors. Data were analyzed by SPSS. The major results were: There was a need for consumers to improve the front fastener type, cuff fastener, mesh patch position, and raincoat pocket position. The most important parameter to choose raincoat fabric was waterproof and the other parameters were vapor-porous/water repellent, design, color, fashionability, air-permeability and easy-put on/off. The pattern image sensibility of jacquard fabrics was explained by seven factors: gorgeous, simple, cute, futuristic, ethnic, feminine, and cool. A higher pattern preference was found in the jacquard fabrics of unique, sporty, natural, luxurious, and trendy images. The pattern preference was predicted at 45.3% with gorgeous, simple, pure, cute, futuristic factors. The correlation coefficient between the pattern image sensibility factor 1 (gorgeous) and pattern preference was 0.674 and with factor 3 (cute) was 0.416, and with factor 6 (cool) was 0.209. The 4 factors (gorgeous, simple, cute, futuristic) were selected as a significant pattern image sensibility that influenced preference.

• Toxicological Research
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• v.32 no.4
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• pp.337-343
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• 2016

We determined the toxicity of mixtures of ethyl acetate (EA), isopropyl alcohol (IPA), methyl ethyl ketone (MEK), toluene (TOL) and xylene (XYL) with half-maximal effective concentration ($EC_{50}$) values obtained using human hepatocytes cells. According to these data, quantitative property-activity relationships (QPAR) models were successfully proposed to predict the toxicity of mixtures by multiple linear regressions (MLR). The leave-one-out cross validation method was used to find the best subsets of descriptors in the learning methods. Significant differences in physico-chemical properties such as boiling point (BP), specific gravity (SG), Reid vapor pressure (rVP) and flash point (FP) were observed between the single substances and the mixtures. The $EC_{50}$ of the mixture of EA and IPA was significantly lower than that of contained TOL and XYL. The mixture toxicity was related to the mixing ratio of MEK, TOL and XYL (MLR equation $EC_{50}=3.3081-2.5018{\times}TOL-3.2595{\times}XYL-12.6596{\times}MEK{\times}XYL$), as well as to BP, SG, VP and FP (MLR equation $EC_{50}=1.3424+6.2250{\times}FP-7.1198{\times}SG{\times}FP-0.03013{\times}rVP{\times}FP$). These results suggest that QPAR-based models could accurately predict the toxicity of polar and nonpolar mixtures used in rotogravure printing industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1675-1682
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• 2012

Aim: The study aim was to determine the frequency with which women decline clinicians' treatment recommendations and variations in this frequency by age, cancer and service descriptors. Design: The study included 36,775 women diagnosed with early invasive breast cancer in 1998-2005 and attending Australian and New Zealand breast surgeons. Rate ratios for declining treatment were examined by descriptor, using bilateral and multiple logistic regression analyses. Proportional hazards regression was used in exploratory analyses of associations with breast cancer death. Results: 3.4% of women declined a recommended treatment of some type, ranging from 2.6% for women under 40 years to 5.8% for those aged 80 years or more, and with parallel increases by age presenting for declining radiotherapy (p<0.001) and axillary surgery (p=0.006). Multiple regression confirmed that common predictors of declining various treatments included low surgeon case load, treatment outside major city centres, and older age. Histological features suggesting a favourable prognosis were often predictive of declining various treatments, although reverse findings also applied with women with positive nodal status being more likely to decline a mastectomy and those with larger tumours more likely to decline chemotherapy. While survival analyses lacked statistical power due to small numbers, higher risks of breast cancer death were suggested, after adjusting for age and conventional clinical risk factors, (1) for women not receiving breast surgery for unstated reasons (RR=2.29; p<0.001); and (2) although not approaching statistical significance $p{\geq}0.200$), for women declining radiotherapy (RR=1.22), a systemic therapy (RR1.11), and more specifically, chemotherapy (RR=1.41). Conclusions: Women have the right to choose their treatments but reasons for declining recommendations require further study to ensure that choices are well informed and clinical outcomes are optimized.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.27-34
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• 2006

In this paper, we propose a combined region-based image retrieval system using EHD(Edge Histogram Descriptor) and CLD(Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., $4{\times}4)$ non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between ELE and CLD, we need to take an $8{\times}8$ inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.6
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• pp.36-45
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• 1998

This paper discusses multiple access control aspects for a wireless extension of high-speed asynchronous transfer mode(ATM) networks. One challenging requirement is that mobile systems convey of diverse types of information including voice, computer data, facsimile, and video data that have different quality of service constraints. Basically, a centralized control scheme for a coordinated multiple access is required in order to efficiently accommodate multimedia traffic on wireless links. The proposed scheme dynamically scheduling the mobile terminal, which has data to transfer according to the service, types at base station. In this scheme, the scheduling algorithm is performed on the basis of traffic descriptors and QoS parameters, which required in the connection set up phase. As a consequence, the transmission scheduling is very flexible and can account for the different traffic rate and delay constraints that emerge from voice and data integration. The simulation result shows that the proposed scheme has better performance over other scheduling algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.12-20
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• 2011

In this paper, we propose an efficient object detection and classification algorithm for video surveillance applications. Previous researches mainly concentrated either on object detection or classification using particular type of feature e.g., Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) etc. In this paper we propose an algorithm that mutually performs object detection and classification. We combinedly use heterogeneous types of features such as texture and color distribution from local patches to increase object detection and classification rates. We perform object detection using spatial clustering on interest points, and use Bag of Words model and Naive Bayes classifier respectively for image representation and classification. Experimental results show that our combined feature is better than the individual local descriptor in object classification rate.

• Journal of the Korean Chemical Society
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• v.48 no.2
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• pp.156-160
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• 2004

The extraction rates of plasticizer of LPG high pressure rubber are studied. Submerging solvents are composed of propane, n-butane, n-pentane, n-hexane, n-heptane, propylene, 1,3-butadiene, 1-pentene, 1-hexene, ethanethiol, t-butanethiol, dimethyl sulfide, methyl ethyl sulfide. The relationship between the extraction rate of plasticizer and the descriptors of submerging solvent by using multiple linear regression is as follows; PE(wt%) = 7.5193 - 0.58500Carbon${\sharp}$ + 2.3294DB + 2364SH, (N = 13, F = 24.135, R$^2$ = 0.8894, R$_{adj}^2$ = 0.8526, Variance = 7.588) Plasticizer is well extracted by LPG composed of the high vapour pressure and polarity compounds. The mass of extracted plasticizer becomes increasing in proportion to the contents of thiol sulfur compound and unsaturated hydrocarbon such as propylene and 1,3-butadiene in LPG. While the heavier hydrocarbons are, the less the quantities of plasticizer extracted from rubber are.

• Bulletin of the Korean Chemical Society
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• v.32 no.12
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• pp.4397-4402
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• 2011

The NF-${\kappa}B$ system of transcription factors plays a crucial role in inflammatory diseases, making it an important drug target. We combined quantitative structure activity relationships for predicting the activity of new compounds and quantitative dynamic models for the NF-${\kappa}B$ network with intracellular concentration models. GFA-MLR QSAR analysis was employed to determine the optimal QSAR equation. To validate the predictability of the $IKK{\beta}$ QSAR model for an external set of inhibitors, a set of ordinary differential equations and mass action kinetics were used for modeling the NF-${\kappa}B$ dynamic system. The reaction parameters were obtained from previously reported research. In the IKKb QSAR model, good cross-validated $q^2$ (0.782) and conventional $r^2$ (0.808) values demonstrated the correlation between the descriptors and each of their activities and reliably predicted the $IKK{\beta}$ activities. Using a developed simulation model of the NF-${\kappa}B$ signaling pathway, we demonstrated differences in $I{\kappa}B$ mRNA expression between normal and different inhibitory states. When the inhibition efficiency increased, inhibitor 1 (PS-1145) led to long-term oscillations. The combined computational modeling and NF-${\kappa}B$ dynamic simulations can be used to understand the inhibition mechanisms and thereby result in the design of mechanism-based inhibitors.