• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.177-186
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• 2018

The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.

• Journal of the Korea Society for Simulation
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• v.15 no.4
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• pp.119-128
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• 2006

A turnout system which permits trains to pass from one track to another is a combination of the switch, the crossing, lead rails which are necessary to connect the switch and the crossing, two guard rails and a switch machine for operating the switch. A turnout is the sole moving part among the railway components and has complex configuration, so the safety has always been raised an issue. In Korea, it is planned to adopt the high speed tilting train, which operates at the maximum speed of 200km/h, at conventional lines by the year of 2010. However, for the application of the tilting train to conventional lines, it is prerequisite to establish a stable turnout system allowing the tilting train to pass through it without reducing speed. Therefore, the improved turnout system for the speed-up of conventional lines has been developed and the prototype of the turnout system has been constructed. In this study, simulation of noise and vibration around the improved turnout system was performed in order to predict the generation level of noise and vibration due to passing of the tilting train through the turnout system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.909-916
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• 2009

This paper presents the automatic vision algorithm to generate and calibrate reference phase plane to improve the accuracy of 3D measuring machine of using phase shifting projection moire method, which is not traditional N-bucket method, but is based on direct image processing method to the pattern projection image. Generally, to acquire accurate reference phase plane, the calibration specimen with well treated surface is needed, and detailed calibration method should be performed. For the cost reduction of specimen manufacturing and the calibration time reduction, on the specimen, not specially designed, with general accuracy level, an efficient calibration procedure for the reference phase generation is proposed. The proposed vision algorithm is developed to extract the line center points of the projected line pattern from acquired images, derive the line feature information consisting of its slope and intercept by using sampled feature points, and finally generate the related reference phase between line pairs. Experimental results show that the proposed method make reference phase plane with a good accuracy under noisy environment and the proposed algorithm can reduce the total cost to make high accurate calibration specimen, also increase the accuracy of reference phase plane, and reduce the complex calibration procedure to move grid via N-bucket algorithm precisely.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.72-88
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• 2020

Recent rapid advances in computer hardware performance have led to relatively low computational costs, increasing the number of applications of machine-learning techniques to geophysical problems. In particular, deep-learning techniques are gaining in popularity as the number of cases successfully solving complex and nonlinear problems has gradually increased. In this paper, applications of seismic data denoising methods using deep-learning techniques are introduced and investigated. Depending on the type of attenuated noise, these studies are grouped into denoising applications of coherent noise, random noise, and the combination of these two types of noise. Then, we investigate the deep-learning techniques used to remove the corresponding noise. Unlike conventional methods used to attenuate seismic noise, deep neural networks, a typical deep-learning technique, learn the characteristics of the noise independently and then automatically optimize the parameters. Therefore, such methods are less sensitive to generalized problems than conventional methods and can reduce labor costs. Several studies have also demonstrated that deep-learning techniques perform well in terms of computational cost and denoising performance. Based on the results of the applications covered in this paper, the pros and cons of the deep-learning techniques used to remove seismic noise are analyzed and discussed.

• Journal of agricultural medicine and community health
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• v.23 no.1
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• pp.3-14
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• 1998

To investigate the actual states of farmers syndrome and their related factors, the author surveyed a total of 534 rural residents, resided in Puyeo kun, Chungnam Province, during August 1996. The data were collected from members of an association and their families of agricultural co-operatives, and analysed. Following are the results summarized therefrom; 1. The prevalence rate of farmers syndrome as a whole was 36.7%, but that of female was higher as 45.0% than male as 27.4%. 2. The prevalence rates of farmers syndrome were higher in the group of higher age, shorter education years, longer farming careers, and longer daily farming hours. 3. The prevalence rates of farmers syndrome did not show statistically significantly different among groups of farming categories such as specialize in farming, such as side line, and not farming. 4. Sex, age, and daily farming hours were proved to be a related factors of farmers syndrome by logistic regression analysis. Odds ratio of female group was 2.06 compared with male group, above 70 years age group was 6.24 compared with below 40 years age group, and group of farming more than 8 hours a day was 2.55 compared with not farming group. 5. The mean scores of self-estimated health states of the group with farmers syndrome was lower than those with negative or suspicious farmers syndrome, but the mean scores of psychological symptoms, other than symptoms of farmers syndrome was statistically significantly higher in farmers syndrome group. Consequently, farmers syndrome seems to be not disease entity but symptom complex which is highly related with age and sex. For that reason, there is a need of study on the differentiate the farmers syndrome and chronic musculoskeletal diseases in the aged.

• The Journal of Korean Academy of Prosthodontics
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• v.45 no.6
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• pp.780-786
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• 2007

Statement of problem. The performance and maintenance of implant-supported prostheses are primarily dependent upon load transmission both at the bone-to-implant interface and within the implant-abutment-prosthesis complex. The design of the interface between components has been shown to have a profound influence on the stability of screw joints. Purpose. The Purpose of this study was to compare the strength and the fatigue resistance of 1-piece and 2-piece abutment connected to oral implant, utilizing an internal conical interface. Material and methods. Twenty $Implatium^{(R)}$ tapered implants were embedded to the top of the fixture in acrylic resin blocks. Ten $Combi^{(R)}$(1-piece) and $Dual^{(R)}$(2-piece) abutments of the same dimension were assembled to the implant, respectively. The assembled units were mounted in a testing machine. A load was applied perpendicular to the long axis of the assemblies and the loading points was at the distance of 7mm from the block surface. Half of 1-piece and 2-piece abutment-implant units were tested for the evaluation of the bending strength, and the others were cyclically loaded for the evaluation of the fatigue resistance until plastic deformation occurred. Nonparametric statistical analysis was performed for the results. Results. Mean plastic and maximum bending moment were $1,900{\pm}18Nmm,\;3,609{\pm}106Nmm$ for the 1-piece abutment, and $1,250{\pm}31Nmm,\;2,688{\pm}166Nmm$ for the 2-piece abutment, respectively. Mean cycles and standard deviation when implant-abutment joint showed a first plastic deformation were $238,610{\pm}44,891$. cycles for the 1-piece abutment and $9,476{\pm}3,541$ cycles for the 2-piece abutment. A 1-piece abutment showed significantly higher value than a 2-piece abutment in the first plastic bending moment (p<.05), maximum bending moment (p<.05) and fatigue strength (p<.05). Conclusion. Both 1-piece and 2-piece conical abutment had high strength and fatigue resistance and this suggests long-term durability without mechanical complication. However, the 1-piece conical abutment was more stable than the 2-piece conical abutment in the strength and the fatigue resistance.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.361-368
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• 2014

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.381-388
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• 2020

This paper discusses English Hedges or Hedging Expressions on the basis of their morphosyntactic-pragramatic properties within the perspective of sociolinguistics. The term, 'Hedges' for the past decades since Lakoff(1973), has received little attention from the English grammar circles such as morphosyntax and the generative grammar theories. This paper presents a more comprehensive approach to the identification, distributions, functions, and the morphosyntactic properties of English Hedges. The earlier research on English Hedges in the 70's show that hedges are metalinguistic or mitadiscourse expressions which constitute a means for executing Politeness strategy in pragmatics. Nonetheless, research from the interface of syntactic-pragmatics has been scarce. This article suggests a more complex body of English hedges that have not been extensively discussed in the literature. Additionally, their configurational domain is to be proposed as part of the PolP with [±hedged] above CP⁺ (or CP beyond). The ramifications of the current study are suggested in terms of comparative linguistics, EFL/ESL studies of English for global communication, and pragmatics-sensitive machine translation studies in the forseeable future.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.145-154
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• 2009

High spatial resolution satellite image classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, the extraction of the spatial information is one of the most important steps in high resolution satellite image classification. This study proposes a new spatial feature extraction method, named SSI(Shape-Size Index). SSI uses a simple region-growing based image segmentation and allocates spatial property value in each segment. The extracted feature is integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a SVM(Support Vector Machines) classifier. In order to evaluate the proposed feature extraction method, KOMPSAT-2 and QuickBird-2 data are used for experiments. It is demonstrated that proposed SSI algorithm leads to a notable increase in classification accuracy.