• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.3 no.3
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• pp.45-76
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• 2000

The purposes of this study were to investigate and understand the present status of various types of "deserts", such as sand desert, gravel desert, rock desert, earth desert, salt desert, desert, rocky desert, gobi desert, sandy desert, clay desert, etc., and the general countermeasures for the combating "desertification" "desertization", and to develop the technologies on the revegetation and restoration for the combating desertification in China. The methods of this study were mainly composed of field surveys on the several experimental sites and research institutes related to combating desertification in China, and examinations on the various technologies for the combating desertification at the Daxing Experimental Station of Beijing Forestry University. The conclusion from this study may be summarized as follows; 1. Status and tendency of desertification in China : China is one of the countries seriously threatened by desertification. Desertification affected areas in China are mainly distributed in arid, semi-arid and dry sub-humid areas in China, covering the most regions of the Northeast China (eastern region of Inner-Mongolia), the northern part of the North China (middle and western region of Inner-Mongolia, Shaanxi, Ningsha, Gansu) and the western part of the Northwest China (Xinzang, Qinghai, Xizang). The total area affected by desertification in China is approximately 2.622 million $km^2$. It covers 27.3% of the total territory of China. Until recently, it is estimated that the annual spreading ratio of desertification in China is 2,460 $km^2$. Therefore, desertification is mostly serious problems facing to the Chinese people. 2. The causes and environmental effect of desertification : The desertification in China is mainly caused by compound factors, including natural condition and human activities. In China, the desertification is started by the decrease of precipitation, continuous dry and drought, strong wind, wind and water erosion, land degradation and loss of natural vegetation caused by climate variation, and accelerated by the human activities, such as over-cultivating, over-grazing, over-cutting of woods, irrational use of water resources. Because desertification has affected the geographical features, soil nutrients contents, salinity, vegetation coverage and the functions of ecosystem, the environmental deteriorations in the desertification affected areas are very seriously. 3. The fundamental strategies of combating desertification in China are the increase of education and awareness of people through various mass media, the revision of laws to guarantee operation of Desertification Combating Law and to improve many relating laws and regulations, the application of advanced technologies and training of experts, the establishment of discriminative policies, and increasing arrangement of budget-investment, and so on. China, as a signed country in UNCCD, has made efforts for the combating desertification. Korea is also signed country in UNCCD, so we should play an important role in the desertification combating projects of China for the northest asia and global environmental conservation as well as environmental conservation of Korea.

• Korean Journal of Heritage: History & Science
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• v.53 no.2
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• pp.156-179
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• 2020

The Preservation Decree (1933) is the basic law relevant to the conservation of cultural property of colonial Chosun, and invoked clauses from the Old History Preservation Act (1897), the Historic Scenic Sites Natural Monument Preservation Act (1919), and the National Treasure Preservation Act (1929), which were all forms of Japanese Modern Cultural Heritage Law, and actually used the corresponding legal text of those laws. Thus, the fact that the Preservation Decree transplanted or imitated the Japanese Modern Cultural Heritage Law in the composition of the constitution can be proved to some extent. The main features and characteristics of the Preservation Decree are summarized below. First, in terms of preservation of cultural property, the Preservation Decree strengthened and expanded preservation beyond the existing conservation rules. In the conservation rules, the categories of cultural properties were limited to historic sites and relics, while the Preservation Decree classifies cultural properties into four categories: treasures, historic sites, scenic spots, and natural monuments. In addition, the Preservation Decree is considered to have advanced cultural property preservation law by establishing the standard for conserving cultural property, expanding the scope of cultural property, introducing explicit provisions on the restriction of ownership and the designation system for cultural property, and defining the basis for supporting the natural treasury. Second, the Preservation Decree admittedly had limitations as a colonial cultural property law. Article 1 of the Preservation Decree sets the standard of "Historic Enhancement or Example of Art" as a criteria for designating treasures. With the perspective of Japanese imperialism, this acted as a criterion for catering to cultural assets based on the governor's assimilation policy, revealing its limitations as a standard for preserving cultural assets. In addition, the Japanese imperialists asserted that the cultural property law served to reduce cultural property robbery, but the robbery and exporting of cultural assets by such means as grave robbery, trafficking, and exportation to Japan did not cease even after the Preservation Decree came into effect. This is because governors and officials who had to obey and protect the law become parties to looting and extraction of property, or the plunder and release of cultural property by the Japanese continued with their acknowledgement,. This indicates that cultural property legislation at that time did not function properly, as the governor allowed or condoned such exporting and plundering. In this way, the cultural property laws of the Japanese colonial period constituted discriminative colonial legislation which was selected and applied from the perspective of the Japanese government-general in the designation and preservation of cultural property, and the cultural property policy of Japan focused on the use of cultural assets as a means of realizing their assimilation policy. Therefore, this suggests that the cultural property legislation during the Japanese colonial period was used as a mechanism to solidify the cultural colonial rules of Chosun and to realize the assimilation policy of the Japanese government-general.