• Journal of the Korean Geotechnical Society
• /
• v.17 no.4
• /
• pp.127-134
• /
• 2001

본 논문에서는 국내 여러 지역에서 수행된 도로, 철도 및 기타 토목공사를 위한 설계과정에서 조사가 이루어진 현지조사와 시추코아 및 시추공을 대상으로 암반평가가 이루어진 자료들을 대상으로 암반분류방법간의 상관관계에 대해 조사하였다. 상관관계에 대한 해석은 암반분류에서 많이 사용되고 있는 RMR과 Q분류법간의 상관관계 그리고 RQD와 두 암반평가방법간의 관계에 대하여 암석성인별 분류 즉 화성암, 퇴적암 및 변성암별로 검토를 실시하였다. 전체적으로 분류방법의 상관관계는 좋게 나타나고 있다. 그리고 음파검층에 의한 탄성파 P파 속도와 RMR의 상관관계를 고찰하였는데, 이 두 요소간의 상관성은 비교적 양호하였으며 보다 신뢰성 있는 관계식을 유도하기 위한 노력이 필요하다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.271-278
• /
• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital sign processing approach. From the 20 seconds query audio file, the audio signal is segmented into 23ms frame with non-overlapped hamming window and 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS(Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we can verify the superior performance of the proposed method that provides near 90% success rate for the genre classification which means 10%∼20% improvements over the previous methods. For the case of actual user system environment, feature vector is extracted from the random interval of the query audio and it shows overall 80% success rate except extreme cases of beginning and ending portion of the query audio file.

• Tunnel and Underground Space
• /
• v.28 no.3
• /
• pp.232-246
• /
• 2018

In this study, the relationship between in situ seismic wave velocities and RMR (rock mass rating) was investigated in a test bed for the examination of the basis of rock classification (RMR) based on seismic wave velocity. The seismic wave velocity showed a monotonous increase with depth. It was also found that there was no systematic correlation between the seismic wave velocity (Vp) and other parameters (RQD, joint spacing, UCS, rock core Vp, and RMR) collected at the same depth of the same borehole. However, correlative relation was observed among RMR, RQD, and joint spacing. On the other hand, when all the data in the borehole (three holes) are examined without considering the depth, Vp still shows no correlation with RMR parameters (e.g., correlative coefficient for uniaxial compressive strength and joint spacing are 0.039 and 0.091, respectively), but Vp shows weak correlative relation with RMR and RQD (correlative coefficient for RQD and RMR are 0.193 and 0.211, respectively). Thus, it is found that it is difficult to deduce physical properties of rock mass directly from seismic wave velocities, but the seismic wave velocity can be used as a tool to approximate rock mass properties because of weaker correlation between Vp and RMR with RQD. In addition, the velocity value of for soft and moderate rocks suggested by widely used construction standards is slower than that of the observed velocity, implying that the standards need to be examined and revised.

• Geomechanics and Engineering
• /
• v.21 no.6
• /
• pp.503-511
• /
• 2020

This study evaluated the side resistance of drilled shafts socketed into rock sections. Commonly used analysis methods for side resistance of piles in rocks are examined by utilizing a large number of load test data. The analysis of the unit side resistance of pile foundations embedded into rock sections is based on an empirical coefficient (α) and the uniaxial compressive strength (q_u) or its root (${\sqrt{q_u}}$). The Davisson criterion was used to interpret the resistance capacity from the load test results to acquire the computed relationships. The α-${\sqrt{q_u}}$ relationship is proven to be reliable in the prediction of friction resistance. This study further analyzed the relationship by including the effect of rock quality designation (RQD) on the results. Analysis results showed that the analysis model of α-${\sqrt{q_u}}$-RQD provided better prediction and reliability considering the RQD classification. Based on these analyses, the side resistance of drilled shafts socked into rocks is provided with statistical data to support the analysis.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.7 no.2
• /
• pp.12-20
• /
• 2004

This study was carried out from January 1998 to December 1999 to report the revegetation of cutting-rock slopes and a design standard in the highway cut-slopes. The field data was collected from the 67 sites cutting-rock slopes of highways, local roads, and field test. As the result of analyze, cutting-rock slopes revegetation measures were 16 types. There were Vine planting(3 types), Hydroseeding measures with seed-fertilizer-soil materials(5 types), Vegetaion-base spraying measures(5 types), and Stability measures(3 types). The factors affecting the plant coverage rates of cutting-rock slopes were the slope gradient, the slope width and direction. The plant coverage rate decreases in the condition of steep slope and long slope width and length(height). In addition, the plant coverage rates of the westward and southward were lower than that of the northward and eastward. Most dominant species were Zoysia japonica, Lespedeza cyrtobotrya, Lespedeza cuneata, Rubus crataegifolius, Miscanthus sinensis, Arrundinella hirta, Themeda triandra, and Oenothera odorata. Exotic species were Eragrostis curvula(Weeping lovegrass), Dactylis glomerata Orchardgrass), Lolium perenne(Perennial ryegrass), and Festuca arundinacea(Tall fescue). It is recommended to adjust the proposed factor as environment, topsoil, classification of rock, field condition and characteristic related with revegetation measures on slopes for the presentation of revegetation standard.

• Journal of Software Assessment and Valuation
• /
• v.15 no.1
• /
• pp.63-70
• /
• 2019

These days, as Interest in Image recognition with deep learning is increasing, there has been a lot of research in image recognition using deep learning. In this study, we propose a system for classifying rocks through rock images of 18 types of rock(6 types of igneous, 6 types of metamorphic, 6 types of sedimentary rock) which are addressed in the high school curriculum, using CNN model based on Tensorflow, deep learning open source framework. As a result, we developed a classifier to distinguish rocks by learning the images of rocks and confirmed the classification performance of rock classifier. Finally, through the mobile application implemented, students can use the application as a learning tool in classroom or on-site experience.

• Tunnel and Underground Space
• /
• v.19 no.6
• /
• pp.479-489
• /
• 2009

Appropriate investigation of ground condition near excavation face in tunnelling is an inevitable process for safe and economical construction. In this study mechanical parameters from drilling process for blasting were investigated for the purpose of predicting the ground condition, especially rock mass strength, ahead of tunnel face. Rock mass strength is one of the most important factors for classification of rock mass and making a decision of support type in underground construction. Several rock specimens which are considered homogeneous and having different strength values respectively were tested by hydraulic drill machines generally used. As a result, penetration rate is fairly related with rock mass strength among drilling parameters. It is also found that penetration rate increases along with the higher impact pressure even under same rock strength condition. It is finally suggested that new prediction method for rock mass strength using percussive pressure and penetration rate during drilling work can be utilized well in construction site.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.13 no.4
• /
• pp.215-223
• /
• 2018

Recently, as interest of wearable devices has increased, commercially available smart wristbands and applications have been used as a tool for personal healthy management. However most previous studies have focused on evaluating the accuracy and reliability of the technical problems of wearable devices, especially step counts, walking distance, and energy consumption measured from the smart wristbands. In this study, we propose a physical activity evaluation model using classification rules, induced from the associative classification mining approach. These rules associated with five physical activities were generated by considering activities and walking times in target heart rate zones such as 'Out-of Zone', 'Fat Burn Zone', 'Cardio Zone', and 'Peak Zone'. In the experiment, we evaluated the prediction power of classification rules and verified its effectiveness by comparing classification accuracies between the proposed model and support vector machine.

• Journal of the Korean Geophysical Society
• /
• v.7 no.4
• /
• pp.293-304
• /
• 2004

• Korean Journal of Remote Sensing
• /
• v.6 no.2
• /
• pp.115-133
• /
• 1990

Study was made on the spectral characteristics of rock samples including bentonites collected from the northern Ulsan area. The geology of the area consists mainly of sediments of the Kyongsang Series and Bulguksa granite, the Tertiary volcanics, andesites and tuffs. Relative reflectances of meshed samples(2.5~10mm) to BaSO$_4$ are measured at 6 Landsat TM spectral windows (excluding the thermal band) with HHRR, and their reflection charactristics were analysed. In addition, three different data selection schemes including the Eulidean distance, multiple regression, and PCA weight methods were applied to the 30 TM ratio channels, derived from the above 6 bands. The selected data sets were subject to two unsupervised classification techniques(FA and ISODATA) in order to compare the effectiveness for classification of particularly bentonite from others. As a result, in ISODATA analysis the multiple regression model shows the best, followed by the Euliean distances one. The PCA weight model seems to show some confusion. In FA, though difficult for quantitative analysis, the best still seems to be the regression model. Among ratio bands, rations of band 7 or 5 against other bands represent the best contribution in classification of bentonites from others.