• Journal of Biosystems Engineering
• /
• v.37 no.4
• /
• pp.265-270
• /
• 2012

Purpose: An analytic method to design excitation and emission filters of a multispectral fluorescence imaging system is proposed and was demonstrated in an application to poultry fecal inspection Methods: A mathematical model of a multispectral imaging system is proposed and its system parameters, such as excitation and emission filters, were optimally determined by linear discriminant analysis (LDA). An alternating scheme was proposed for numerical implementation. Fluorescence characteristics of organic materials and feces of poultry carcasses are analyzed by LDA to design the optimal excitation and emission filters for poultry fecal inspection. Results: The most appropriate excitation filter was UV-A (about 360 nm) and blue light source (about 460 nm) and band-pass filter was 660-670 nm. The classification accuracy and false positive are 98.4% and 2.5%, respectively. Conclusions: The proposed method is applicable to other agricultural products which are distinguishable by their spectral properties.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.4
• /
• pp.383-390
• /
• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Korean Journal of Applied Biomechanics
• /
• v.28 no.1
• /
• pp.61-67
• /
• 2018

Objective: The human body is often modelled as a spring-mass system. Lower extremity stiffness has been considered to be one of key factor in the performance enhancement of running, jumping, and hopping involved sports activities. There are several different classification of lower extremity stiffness consisting of vertical stiffness, leg stiffness, joint stiffness, as well as muscle and tendon stiffness. The primary purpose of this paper was to review the literature and describe different stiffness models and discuss applications of stiffness models while engaging in sports activities. In addition, this paper provided a current update of the lower extremity literature as it investigates the relationships between lower extremity stiffness and both functional performance and injury. Summary: Because various methods for measuring lower extremity stiffness are existing, measurements should always be accompanied by a detailed description including type of stiffness, testing method and calculation method. Moreover, investigator should be cautious when comparing lower extremity stiffness from different methods. Some evidence highlights that optimal degree of lower extremity stiffness is required for successful athletic performance. However, the actual magnitude of stiffness required to optimize performance is relatively unexplored. Direct relationship between lower extremity stiffness and lower extremity injuries has not clearly been established yet. Overall, high stiffness is potentially associate risk factors of lower extremity injuries although some of the evidence is controversial. Prospective injures studies are necessary to confirm this relationship. Moreover, further biomechanical and physiological investigation is needed to identify the optimal regulation of the lower limb stiffness behavior and its impact on athletic performance and lower limb injuries.

• Journal of Animal Science and Technology
• /
• v.65 no.2
• /
• pp.365-376
• /
• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.24 no.3
• /
• pp.289-297
• /
• 2006

The Land Cover/Land Use Map have been constructed from 1998, which has hierarchical structure according to land cover/land use system. Level 1 classification Map have done using Landsat satellite image over whole Korean peninsula. Level II classification Map have been digitized using IRS-1C, 1D, KOMPSAT and SPOT5 satellite images resolution-merged with low resolution color images. Level II Land Cover/Land Use Map construction by digitizing method, however, is consuming enormous expense for satellite image acquisition, image process and Land Cover/Land Use Map construction. In this paper, the possibility of constructing Level II Land Cover/Land Use Map using hyperspectral satellite image of EO-1 Hyperion, which is studied a lot recently, is studied. The comparison of classifications using Hyperion satellite image offering more spectral information and Landsat-7 ETM+ image is performed to evaluate the availability of Hyperion satellite image. Also, the algorithm of the optimal band selection is presented for effective application of hyperspectral satellite image.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.545-557
• /
• 2022

This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images. Land cover map was calculated based on the SVM supervised classification technique. In order to find the optimal combination to classify the DMZ wildfire damage area, supervised classification according to various kernel and band combinations in the SVM was performed and the accuracy was evaluated through the error matrix. Verification was performed by comparing the results of the wildfire detection based on satellite image and data by the wildfire statistical annual report in 2020 and 2021. Also, wildfire damage areas was detected for which there is no current data in 2022. This is to quickly determine reliable results.

• Journal of the Korean Society of Costume
• /
• v.60 no.9
• /
• pp.95-105
• /
• 2010

The purpose of this study is in allowing thinking about the design development process which is more towards the visual and perceptional aspects related to the form structure by more diverse methods by typology of idea generation. To accomplish such goal, researches in the psychology, pedagogy, engineering, and consilient studies as well as related precedent researches and reference data in architecture, promotion, industrial design, and other art fields and fashion designs are collected and analyzed to see the study trend. In addition, in the content analysis method based on such, the idea generation was classified into types in consideration of relevancy, usefulness, and suitability with fashion. First, a concentrated thinking of a limited space is a method of leading an optimal design by focusing on solving the cause of a problem within a space which generates the problem. Second, plan thinking per section of structure decomposition is a method of dismantling the design problems per organization, thinking type, factor, and characteristic into sub-modules to re-interpret and re-organize the problems in various aspects. Third, an associated thinking through interpreting relationships among vocabularies is a method of selecting the marginal languages that allow a person to come up with concrete forms and the key words related to fashion to import the characteristics and attributes of the marginal languages and thematic relationship between the two terms to search the relevancy. Lastly, the free integrated thinking of language extension is a method of groping integration between other fields and fashion by free integration among the extended terms by extending the vocabularies through inferring metaphorical expressions founded upon individual's memories or knowledge concepts regarding theme words that do not allow concrete forms to come up.

• Journal of Korean Foot and Ankle Society
• /
• v.16 no.2
• /
• pp.87-93
• /
• 2012

Fractures located at the metaphyseal/diaphyseal junction at the base of the fifth metatarsal were first described by Sir Robert Jones in 1902. However, ever since, there has been disagreement and debate regarding the diagnosis, classification, pathomechanics, the incidences, and potential causes of delayed unions and nonunions, and the optimal method of treatment. It appears to be widely agreed that proximal fractures of the metaphyseal/diaphyseal region of the fifth metatarsal are prone to delayed union or even nonunion. Several classifications of proximal fifth metatarsal stress fractures have been devised. Torg et al. classified fractures involving the proximal part of the diaphysis of the fifth metatarsal into three types. The Torg classification is a good grading system that can be used to determine the type of surgery needed as well as for the prediction of prognosis. The ''plantar gap'' might add to the decision-making process for surgery and improve the prediction of patient prognosis. In addition, the new classification using 'plantar gap' might be used for classification of fifth metatarsal stress fracture. Fifth metatarsal stress fractures can be treated conservatively or surgically, and excellent results have been reported for surgery with rapid recovery in athletes. Intramedullary screw fixation has become a popular form of fixation for fifth metatarsal stress fractures. Bone grafting presents the problems of a longer recovery time and additional skin incision for harvesting. The modified tension band wiring is an useful and simple option for surgical treatment of challenging fifth metatarsal stress fractures.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.22 no.5
• /
• pp.575-589
• /
• 2020

In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disc cutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time of the disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To do this, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructed are used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm are all classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classification performance evaluation index was compared and analyzed.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.3
• /
• pp.149-154
• /
• 2017

The MCT-Adaboost algorithm chooses an optimal set of features in each rounds. On each round, it chooses the best feature by calculate minimizing error rate using feature index and MCT kernel distribution. The involved process of weak classification executed by a hard decision. This decision occurs some problems when it chooses ambiguous kernel feature. In this paper, we propose the modified MCT-Adaboost classification using soft decision. The typical MCT-Adaboost assigns a same initial weights to each datum. This is because, they assume that all information of database is blind. We assign different initial weights with our propose new algorithm using some statistical properties of involved features. In experimental results, we confirm that our method shows better performance than the traditional one.