• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Earthquakes and Structures
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• v.7 no.6
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• pp.1141-1169
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• 2014

This study is aimed to investigate the seismic performance of low-rise precast wall system with base isolation. Three types of High Damping Rubber Bearing (HDRB) were designed to provide effective isolation period of 2.5 s for three different kinds of structure in terms of vertical loading. The real size HDRB was manufactured and tested to obtain the characteristic stiffness as well as damping ratio. In the vertical stiffness test, it was revealed that the HDRB was not an ideal selection to be used in isolating lightweight structure. Time history analysis using 33 real earthquake records classified with respective peak ground acceleration-to-velocity (a/v) ratio was performed for the remaining two types of HDRB with relatively higher vertical loading. HDRB was observed to show significant reduction in terms of base shear and floor acceleration demand in ground excitations having a/v ratio above $0.5g/ms^{-1}$, very much lower than the current classification of $0.8g/ms^{-1}$. In addition, this study also revealed that increasing the damping ratio of base isolation system did not guarantee better seismic performance particularly in isolation of lightweight structure or when the ground excitation was having lower a/v ratio.

• Journal of Biosystems Engineering
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• v.22 no.2
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• pp.210-216
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• 1997

A study to select ground colors of Fuji apple for maturity index which are needed to standardize grading of the apples is presented. Two extreme colors of immature and fully mature Fuji and Zonagold apples produced in Korea were determined. Various ground colors of Fuji apple between the two extreme colors were collected and classified by human vision and colors of Fuji apple for maturity index were selected from the classification. Coordinates of the selected colors in xy chromaticity diagram were determined by spectrophotometers to define them in a standard coordinate system. Coordinates of the colors in r-g chromaticity diagram using a color machine vision system were also determined to use the colors in apple grading by the machine vision system. Grading Fuji apples using the machine vision system was performed and result of the grading was compared with Ending results of human vision and colorimeter. The comparison was performed with the same Fuji apple samples and showed 65% md 75% of same grades, respectively, as the grades determined by the machine vision system. Differences of fading performance between the compared three grading methods were explained as mainly because of the differences of observation area of the grading methods.

• Geomechanics and Engineering
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• v.9 no.3
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• pp.329-344
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• 2015

Cratering tests in rock are generally carried out to identify its fragmentation characteristics. The test results can be used to estimate the minimum amount of explosives required for the target volume of rock fragmentation. However, it is not easy to perform this type of test due to its high cost and difficulty in securing the test site with the same ground conditions as the site where blasting is to be performed. Consequently, this study investigates the characteristics of rock fragmentation by using the hydrocode in the platform of AUTODYN. The effectiveness of the numerical models adopted are validated against several cratering test results available in the literature, and the effects of rock mass classification and ground formation on crater size are examined. The numerical analysis shows that the dimension of a crater is increased with a decrease in rock quality, and the formation of a crater is highly dependent on a rock of lowest quality in the case of mixed ground. It is expected that the results of the present study can also be applied to the estimation of the level and extent of the damage induced by blasting in concrete structures.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.131-137
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• 2013

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.203-212
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• 2023

Directional felling is a felling method practised by the Forestry Department in Peninsular Malaysia as prescribed in Field Work Manual (1997) for Selective Management Systems (SMS) in forest harvesting. Determining the direction of tree felling in Peninsular Malaysia is conducted during the pre-felling inventory 1 to 2 years before the felling operation. This study aimed to predict and analyze the direction of tree felling using the vector-based path distance back link method in Geographic Information Systems (GIS) and compare it with the felling direction observed on the ground. The study area is at Balah Forest Reserve, Kelantan, Peninsular Malaysia. A Path Distance Back Link (spatial analyst) function in ArcGIS Pro 3.0 was used in predicting tree felling direction. Meanwhile, a binary classification was used to compare the felling direction estimated using GIS and the tree felling direction observed on the ground. Results revealed that 61.3% of 31 trees predicted using the vector-based projection method were similar to the felling direction observed on the ground. It is important to note that dynamic changes of natural constraints might occur in the middle of tree felling operation, such as weather problems, wind speed, and unpredicted tree falling direction.

• Asian Oncology Nursing
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• v.5 no.1
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• pp.3-10
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• 2005

Back ground: Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule. Purposes: This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower. Methods: The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1-2003.12.31 were assessed by trained nurses. This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed. Results: The data set was created using 165,073 records of 2,228 patients classification database. Main explaining variables were as follows in 3 different data mining techniques. 1) Logistic regression : age, month and section. 2) Decision tree : section, month, age and tumor. 3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month. Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%. Conclusion: The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.

• Journal of the Korean earth science society
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• v.22 no.2
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• pp.138-146
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• 2001

The land cover over Korean peninsula was classified using a multi-temporal NOAA/AVHRR (Advanced Very High Resolution Radiometer) data. Four types of phenological data derived from the 10-day composited NDVI (Normalized Differences Vegetation Index), maximum and annual mean land surface temperature, and topographical data were used not only reducing the data volume but also increasing the accuracy of classification. Self organizing feature map (SOFM), a kind of neural network technique, was used for the clustering of satellite data. We used a decision tree for the classification of the clusters. When we compared the classification results with the time series of NDVI and some other available ground truth data, the urban, agricultural area, deciduous tree and evergreen tree were clearly classified.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.562-571
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• 2006

Mean shear wave velocity of the upper 30m $(V_s30)$ used as the current site classification criterion for determining seismic design ground motions in Korea was established based on the typical depth of site investigations in western US, in which the depth to bedrock is much deeper than that in Korea. In this study, to establish appropriate site classification system for site conditions of Korea, site investigations including in-situ seismic tests to determine shear wave velocity $(V_s)$ were carried out at total 72 sites in Korean peninsula. The mean $V_s's$ to the depths of 5m, 10m, 15m, 20m and 25m together with the $V_s30$ at the testing sites were determined, and the correlation between the mean $V_s$ to a depth shallower than 30m and the $V_s30$ was drawn and suggested for the efficient seismic site classification in Korea. The proposed correlation could be utilized for the seismic design in case of the $V_s$ profiles shallower than 30 m in depth. The correlation in this study, nevertheless, requires further modification by means of the accumulation of various site data in Korea.

• Tunnel and Underground Space
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• v.11 no.2
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• pp.174-181
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• 2001

The cavities created by underground mining, if remained unfilled, can cause ground settlement and surface subsidence as a result of relaxation and breakdown of the carven roof. Construction of structures above the underground mine cavity will have serious problems concerning both structural stability and safely even if the cavity is back-filled. This study was conducted to confirm the location and condition of the cavern as well as the state of the back-fill in A mine area using core logging and borehole camera. The bearing capacity and other mechanical properties of the ground were also measured by the standard penetration test(SPT). Obtained data were used to assess the stability of the ground and the structures to be built by numerical analysis using FLAC. The site investigation results showed that the mine cavities were filled with materials such as boulder and silty sand(SM by unified classification). Result of the numerical analyses indicated that constructing building structures on the over-lying ground above the filled cavities is secure against the potential problems such as surface subsidence and ground settlement.