• Applied Biological Chemistry
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• v.20 no.2
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• pp.210-220
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• 1977

Modified Scheff's Method by Ura is an efficient method used very often in studying quality at a laboratory but, when panels are not well controlled and quality differences among samples are very small, it has sometimes been identified that it is impossible to detect quality differences by this method. Therefore in order to enhance efficiency to rank quality among samples, 'New Modified Scheffe's Method 1' is designed. Experimental results presented in this paper lead to the conclusion that detection is carried out more efficiently by 'New Modified Scheffe's Method 1' than by Modified Scheffe's Method by Ura, and also this title method can be utilized for the aim to train and control panels.

• Journal of Environmental Health Sciences
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• v.32 no.4 s.91
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• pp.254-261
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• 2006

In activity areas of subway workers and passengers in Seoul metropolitan subway lines 1-4, mein concentrations of airborne bacteria were relatively higher in workers' bedroom and station precinct whereas concentrations of particulate matters, $PM_{10}$ and $PM_{2.5}$, were relatively higher in platform, inside train and driver's seat as compared with other activity areas. This result indicates that little correlation between airborne bacteria and particulate matters was found, which assumed that most airborne particulate matters distributed in subway consisted of mainly inorganic dust like a metal particles. Mean concentrations of $PM_{10}$ and $PM_{2.5}$ in station precinct and platform exceeded the threshold limit value ($PM_{10}:150{\mu}g/m^3,\;PM_{2.5}:65{\mu}g/m^3$) but those in station office and ticket office were below it. The genera identified in all the activity areas of subway over 5% detection rate were Staphylococcus, Micrococcus, Bacillus and Corynebacterium, of which Staphylococcus and Micrococcus covered over 50% of total airborne bacteria and were considered as predominant genera distributed in subway.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Journal of IKEEE
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• v.14 no.3
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• pp.224-231
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• 2010

Automatic detection of objects in images has been one of core challenges in the areas such as computer vision and pattern analysis. Especially, with the recent deployment of personal mobile devices such as smart phone, such technology is required to be transported to them. Usually, these smart phone users are equipped with devices such as camera, GPS, and gyroscope and provide various services through user-friendly interface. However, the smart phones fail to give excellent performance due to limited system resources. In this paper, we propose a new scheme to improve object recognition performance based on pre-computation and simple local features. In the pre-processing, we first find several representative parts from similar type objects and classify them. In addition, we extract features from each classified part and train them using regression functions. For a given query image, we first find candidate representative parts and compare them with trained information to recognize objects. Through experiments, we have shown that our proposed scheme can achieve resonable performance.

• Smart Structures and Systems
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• v.26 no.1
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• pp.35-47
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• 2020

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

• Membrane and Water Treatment
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• v.9 no.3
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• pp.181-188
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• 2018

A sidestream contains the filtrate or concentrate from the belt filter press, filter backwash and supernatant from sludge digesters. The sidestream flow, which heads back into the sewage treatment train, is about 1-3% less than the influent flow. However, the sidestream can increase the nutrient load since it contains high concentrations of phosphorus and nitrogen. In this study, the removal of PO4-P with organic matter characteristics and bacteriological changes during the sidestream treatment via ladle furnace (LF) slag was investigated. The sidestream used in this study consisted of 11-14% PO4-P and 3.2-3.6% soluble chemical oxygen demand in influent loading rates. LF slag, which had a relatively high $Ca^{2+}$ release compared to other slags, was used to remove $PO_4-P$ from the sidestream. The phosphate removal rates increased as the slag particle size decreased 19.1% (2.0-4.0 mm, 25.2% (1.0-2.0 mm) and 79.9% (0.5-1.0 mm). The removal rates of dissolved organic carbon, soluble chemical oxygen demand, color and aromatic organic matter ($UV_{254}$) were 17.6, 41.7, 90.2 and 77.3%, respectively. Fluorescence excitation-emission matrices and liquid chromatography-organic carbon detection demonstrated that the sidestream treatment via LF slag was effective in the removal of biopolymers. However, the removal of dissolved organic matter was not significant during the treatment. The intact bacterial biomass decreased from $1.64{\times}10^8cells/mL$ to $1.05{\times}10^8cells/mL$. The use of LF slag was effective for the removal of phosphate and the removal efficiency of phosphate was greater than 80% for up to 100 bed volumes.

• Smart Media Journal
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• v.9 no.2
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• pp.22-32
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• 2020

The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer's disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1166-1169
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• 1998

The Load characteristic of electric railway requires the power demand of the high capacity which amplitude is spacial-temporally fluctuated due to frequent starting and stopping with large tractive force. The conventional electric railway mainly consists of the resistance controlled and the thyristor controlled locomotives, are compensated for their bad characteristics of the power factor$(70\sim80%)$ with installation of another capacitor improving power factor at the substation. Since 1994, VVVF train car with good characteristics of power factor(100%) have been introduced and operated in Kwa-Chon Line. From the present technical tendency, it is judged that introduction of the locomotive with various controlled methods is necessary. The protective equipments installed at the substation are complicated and various aspects to detect faults and reduce their extension, so the universal countermeasures are required. Specially in the case of the fault occurrence it is difficult to calculate the fault location because of the change in the contactline constant according to modifying the characteristics of the contactline (the dualized catenary wire and extension, etc), so much time is required for the detection of fault location. In BT-fed method distance-relays and fault-locators are not installed, we have so many difficulties in the quick accident recovery.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.312-318
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• 2012

Recently, although the need of marine robots being raised in extreme areas, the basis is very deficient. Fortunately, as the robot competition is vitalizing and the need of the robot education is increasing, it is desirable to establish the basis of the R&D and industrialization of marine robots and to train professionals through the development and diffusion of marine robot kits. However, in conventional case, there is no underwater-type autonomous marine robot kit for the marine robot competition, which has the abilities of the underwater locomotion and target detection and avoidance. To solve this problem, a marine robot kit which has the abilities of the underwater locomotion, the waterproof and the weight adjustment, is developed. To verify the performance of the developed kit, test and evaluation such as surge, pitch, yaw, obstacle avoidance is performed. The test and evaluation results show that the possibility of the real applications of the developed kit.