• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of the Korean Society of Radiology
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• v.82 no.2
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• pp.406-416
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• 2021

Purpose To evaluate the association between magnetic resonance imaging (MRI)-based texture parameters and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in patients with non-mucinous rectal cancer. Materials and Methods Seventy-nine patients who had pathologically confirmed rectal non-mucinous adenocarcinoma with or without KRAS-mutation and had undergone rectal MRI were divided into a training (n = 46) and validation dataset (n = 33). A texture analysis was performed on the axial T2-weighted images. The association was statistically analyzed using the Mann-Whitney U test. To extract an optimal cut-off value for the prediction of KRAS mutation, a receiver operating characteristic curve analysis was performed. The cut-off value was verified using the validation dataset. Results In the training dataset, skewness in the mutant group (n = 22) was significantly higher than in the wild-type group (n = 24) (0.221 ± 0.283; -0.006 ± 0.178, respectively, p = 0.003). The area under the curve of the skewness was 0.757 (95% confidence interval, 0.606 to 0.872) with a maximum accuracy of 71%, a sensitivity of 64%, and a specificity of 78%. None of the other texture parameters were associated with KRAS mutation (p > 0.05). When a cut-off value of 0.078 was applied to the validation dataset, this had an accuracy of 76%, a sensitivity of 86%, and a specificity of 68%. Conclusion Skewness was associated with KRAS mutation in patients with non-mucinous rectal cancer.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.7
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• pp.952-961
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• 2003

In the first part of the study, rates of estrus occurrence and success of A.I. service in the Thai-native and Friesian crossbred, and purebred Friesian cows fed in the National Dairy Training and Applied Research Institute in Chiang Mai, Thailand were traced monthly throughout a year. An electric fan and a water sprinkler cooled the stall for the purebred cows during the hot season (March-September). Both rates in pure Friesians were at their highest in the cold-dry season (October- February), but they decreased steadily during the hot-dry season (March-May) and were at their lowest in the hot-wet season (June-September). Seasonal change of a similar pattern was observed in the incidence of estrus, but not in the success rate of insemination in the crossbred cows. By the use of reproductive data, compiled in the same institute, on the 75 % cross- and purebred Friesian cows, and climatological data in Chiang Mai district, effects of ambient temperature and humidity on the reproductive traits of cows were examined by regression analysis in the second half of the study. Significant relationships in the crossbred, expressed by positive-linear and parabola regressions, were found between reproductive parameters such as days to the first estrus (DTFE), A.I. service (DTFAI), and conception, the number of A.I. services required for conception and some climatic factors. However, regarding this, no consistent or intelligible results were obtained in purebred cows, perhaps because electric fans and water sprinklers were used for this breed in the hot season. Among climatic factors examined, the minimum temperature (MINT) in early lactation affected reproductive activity most conspicuously. As the temperature during one or two months prior to the first estrus and A.I. service rose, DTFE and DTFAI steadily became longer, although, when MINT depleted below $17-18^{\circ}C$, the reproductive interval tended to be prolonged again on some occasions. The maximum temperature also affected DTFE and DTFAI, but only in limited conditions. The effect of humidity was not clear, although the inverse relationship between DTFE and minimum humidity during 2 months before the first estrus in the crossbred seemed to be significant. Failure to detect any definite effect of climate on the reproductive traits of pure Friesians seemed to indicate that forced ventilation by electric fans and water sprinklers were effective enough to protect the reproductive ability of this breed from the adverse effects of a hot climate.

• Journal of the Korean Society of Marine Environment & Safety
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• v.16 no.4
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• pp.401-406
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• 2010

Vessel Operator has been navigating with subjective sate distance in accordance with night & daytime, fore & aft, port & stbd abeam and visibility situation. This sate distances may different depending on inside & outside harbor limit, current, wind and visibility situation. By now, the concept of proper sate distance between navigating vessels has been adopted in Korea, using the early 1980's foreign data. And the safe distance is being used with the same value without any consideration of inside & outside harbor and the kind of vessel. So it is necessary to evaluate or search proper distance concept based on different sate consciousness of Korean manners. This paper aims to develop the basic model for marine traffic evaluation and the new model of marine traffic congestion. Also this paper proposes the basic control guideline of vessel traffic service center. The result of this study showed that minimum sate distance should be 4.4L forward, 3.1L aft and 26L abeam in case of good visibility in daytime, considering various parameters such as visibility, day and night. Some differences Here found between the existing minimum sate distance and the new minimum sate distance derived from the result of this study.

• The Korean Journal of Food And Nutrition
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• v.16 no.3
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• pp.192-196
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• 2003

The purpose of this study was to investigate the changes of body composition, serum lipids and several parameters of body fatness (percent body fat, waist-hip ratio) in abdominal women by exercise. For this study, 8-weeks intensive exercise(5km jogging/day, 50min/day) was continued by subjects and they limited only fat rich foods and controlled daily energy intake to 1,800kcal～2,100kcal per day. The subjects were 52 women and the distribution of ages was 36～54 years. The data were analyzed using SPSS/PC package program and the results were estimated by paired t-test, Pearson correlation. The results are summarized as follows : 1) After exercise-training for 8 weeks, percent body fat, body mass index, body weight, total cholesterol was decreased (p<0.05). 2) LDL cholesterol and triglyceride was decreased significantly(p=0.000). The changes in deep abdominal adipose tissue were related to changes in triglycerides. 3) After exercise training, the waist-hip ratio was significantly correlated to body weight and serum lipids. 4) According to the data of this study, Ⅰ recommended that obese women, especially, abdominal obese patients should exercise regularly and we should prolong many studies for obesity.

• PNF and Movement
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• v.5 no.2
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• pp.47-54
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• 2007

Purpose : The purpose of this study were to determine the effect of a Elastic Theraband Exercise Based of PNF L/E pattern on the gait of the chronic Hemiplegic Patients. Methods : We selected the 20 chronic Hemiplegic Patients not given treatment now and divided them into two groups of both 10 Elastic Theraband group and 10 Self Exercise. The first group went through a Elastic Theraband Exercise Based of PNF L/E pattern 30 minutes a day, 5 times a week, for 6 weeks. Exercise used to blue elastic band which 2 patterns of PNF by 1) hip extension - abduction - internal rotation with knee extension. 2) hip flexion - adduction - external rotation with knee flexion. The latter group experienced Self Exercise, 30 minutes a day, 5 times a week, for 6 weeks. Firstly, we measured the absolute improvement of gait velocity(m/s), cadence(steps/min) among walking characters. Secondly, we measured the functional walking ability such as Functional Ambulatory Category(FAC, score out of 5), Modified Motor Assesment Scale(MMAS, score out of 6). Data analysis was performed with using SPSS 12.0 win program. The descriptive analysis was used to obtain average and standard deviation. The independent t-test and the paired t-test were used to compare both the groups about pre and post training test. Treatment effects were established by pre and post assessment. Subjects tolerated the training well without side-effects. Therefore, the results of this study were as follows; Results : 1. There was a more significant improvement of Gait velocity(0.12m/s) Elastic Theraband group(p<.05). 2. There was a more significant improvement of cadence(9.40steps/min) Elastic Theraband group(p<.05). Conclusion : As we can see from above, the findings suggest that Elastic Theraband may be more effective than the Self Exercise for improving some gait parameters such as Gait velocity and Cadency. This conclusion also suggest that Elstic Theraband is more effective for the improvement of gait of chronic Hemiplegic Patients.

• Composites Research
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• v.33 no.3
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• pp.108-114
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• 2020

Piecewise Integrated Composite (PIC) beam is composed of different stacking against loading type depending upon location. The aim of current study is to assign robust stacking sequences against external loading to every corresponding part of the PIC beam based on the value of stress triaxiality at generated reference points using the k-NN (k-Nearest Neighbor) classification, which is one of representative machine learning techniques, in order to excellent superior bending characteristics. The stress triaxiality at reference points is obtained by three-point bending analysis of the Al beam with training data categorizing the type of external loading, i.e., tension, compression or shear. Loading types of each plane of the beam were classified by independent plane scheme as well as total beam scheme. Also, loading fidelities were calibrated for each case with the variation of hyper-parameters. Most effective stacking sequences were mapped into the PIC beam based on the k-NN classification model with the highest loading fidelity. FE analysis result shows the PIC beam has superior external loading resistance and energy absorption compared to conventional beam.

• Phonetics and Speech Sciences
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• v.12 no.3
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• pp.65-71
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• 2020

Feature normalization is a method to reduce the effect of environmental mismatch between the training and test conditions through the normalization of statistical characteristics of acoustic feature parameters. It demonstrates excellent performance improvement in the traditional Gaussian mixture model-hidden Markov model (GMM-HMM)-based speech recognition system. However, in a deep neural network (DNN)-based speech recognition system, minimizing the effects of environmental mismatch does not necessarily lead to the best performance improvement. In this paper, we attribute the cause of this phenomenon to information loss due to excessive feature normalization. We investigate whether there is a feature normalization method that maximizes the speech recognition performance by properly reducing the impact of environmental mismatch, while preserving useful information for training acoustic models. To this end, we introduce the mean and exponentiated variance normalization (MEVN), which is a compromise between the mean normalization (MN) and the mean and variance normalization (MVN), and compare the performance of DNN-based speech recognition system in noisy and reverberant environments according to the degree of variance normalization. Experimental results reveal that a slight performance improvement is obtained with the MEVN over the MN and the MVN, depending on the degree of variance normalization.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.53-63
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• 2015

Singing voice synthesis is the generation of a song using a computer given its lyrics and musical notes. Hidden Markov models (HMM) have been proved to be the models of choice for text to speech synthesis. HMMs have also been used for singing voice synthesis research, however, a huge database is needed for the training of HMMs for singing voice synthesis. And commercially available singing voice synthesis systems which use the piano roll music notation, needs to adopt the easy to read standard music notation which make it suitable for singing learning applications. To overcome this problem, we use a speech database for training context dependent HMMs, to be used for singing voice synthesis. Pitch and duration control methods have been devised to modify the parameters of the HMMs trained on speech, to be used as the synthesis units for the singing voice. This work describes a singing voice synthesis system which uses a MusicXML based music score editor as the front-end interface for entry of the notes and lyrics to be synthesized and a hidden Markov model based text to speech synthesis system as the back-end synthesizer. A perceptual test shows the feasibility of our proposed system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.