• Journal of Korean Society of Environmental Engineers
• /
• v.37 no.5
• /
• pp.253-261
• /
• 2015

In this study, Chlorophyll-a (chl-a) prediction model and multiple parameters affecting algae occurrence in Mulgeum site were evaluated by statistical analysis using water quality, hydraulic and climate data at Mulgeum site (1998~2008). Before the analysis, control chart method and effect period of typhoon were adopted for improving reliability of the data. After data preprocessing step two methods were used in this study. In method 1, chl-a prediction model was developed using preprocessed data. Another model was developed by Method 2 using significant parameters affecting chl-a after data preprocessing step. As a result of correlation analysis, water temperature, pH, DO, BOD, COD, T-N, $NO_3-N$, $PO_4-P$, flow rate, flow velocity and water depth were revealed as significant multiple parameters affecting chl-a concentration. Chl-a prediction model from Method 1 and 2 showed high $R^2$ value with 0.799 and 0.790 respectively. Validation for each prediction model was conducted with the data from 2009 to 2010. Training period and validation period of Method 1 showed 20.912 and 24.423 respectively. And Method 2 showed 21.422 and 26.277 in each period. Especially BOD, DO and $PO_4-P$ played important role in both model. So it is considered that analysis of algae occurrence at Mulgeum site need to focus on BOD, DO and $PO_4-P$.

• Asian-Australasian Journal of Animal Sciences
• /
• v.18 no.1
• /
• pp.22-26
• /
• 2005

Four male crossbred beef steers about 2 years old were used in a 4$\{times}$4 Latin square design to investigate the effect of pelleted sugarcane tops on voluntary feed intake, rumen fermentation and digestibility of nutrients. Experimental treatments were; Control (dried-chopped sugarcane tops (DCST)); PS1 (Pelleted sugarcane tops at 1 cm of diameter); PS2 (Pelleted sugarcane tops at 2 cm of diameter) and PS3 (Pelleted sugarcane tops at 3 cm of diameter). Roughage intake and total dry matter intake were 1.59, 1.62, 1.61, 1.63% BW and 2.09, 2.12, 2.11 and 2.13% BW in control, PS1, PS2 and PS3 treatments, respectively (p<0.05). Digestibility of DM, OM and CP were similar in control and PS3 treatment but there was significant difference (p<0.05) between control and PS1, PS2 treatments. Digestibility of neutral detergent fiber (NDF) and acid detergent fiber (ADF) were 52.89, 50.01, 50.05 and 50.56% and 41.91, 39.96, 39.91 and 39.69% in control, PS1, PS2 and PS3, respectively (p<0.05). Total volatile fatty acids concentrations in rumen contents was 67.68, 65.93, 66.15 and 66.67 mM in control, PS1, PS2 and PS3, respectively (p<0.05). Even though, concentrations of acetate and butyrate (%) were significant different (p<0.05) but concentration of propionate (%) was not affected by treatments (p>0.05). Rumen pH, ammonia nitrogen and plasma urea nitrogen were significantly different (p<0.05) among treatments. From this experiment, it was found that dried-chopped sugarcane tops increased digestibility of nutrients whereas pelleted sugarcane tops increased feed intake in beef cattle. However, pelleted sugarcane tops at 3 cm of diameter did similar result in digestibility and rumen parameters with DCST. Therefore, it could be concluded that pelleting sugarcane top is an alternative way to improve the quality of sugarcane tops for use as ruminant roughage source.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.1
• /
• pp.81-98
• /
• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• Journal of Energy Engineering
• /
• v.25 no.1
• /
• pp.153-162
• /
• 2016

Experimental investigation was carried out on the CHF(Critical Heat Flux) under downward flow condition in narrow rectangular channels simulating subchannel of plate-type-fuel for JRTR(Jordan Research and Training Reactor). The experiments covers the license requirement of the research reactor. Two test sections used in this study simulate full scale subchannels for fission moly uranium target and plate-type-fuel, respectively. From the experimental results, the parameters affecting on the CHF are investigated. By using experimental data, the existing CHF prediction models were evaluated. Finally, the applicability of correlations were analysed to predict CHF in the narrow rectangular channel under the downward flow condition.

• Fire Science and Engineering
• /
• v.33 no.6
• /
• pp.159-164
• /
• 2019

This study aims to minimize casualties in the event of an apartment fire by developing a virtual reality (VR) fire-training program (using the latest IT techniques) and verifying its validity as a learning tool. For this purpose, environmental parameters, such as the unit floor's size and the composition of the family, were established. Then, possible fire scenarios that could occur in the apartment building were developed and implemented in a VR setting. Finally, a survey was conducted to review the responses to the program. The results of the survey demonstrated that the responses were positive, confirming the effectiveness of the program.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.28 no.2
• /
• pp.84-95
• /
• 2017

Attention-deficit hyperactivity disorder (ADHD) is a neuropsychiatric disorder that begins in early childhood and can persist throughout adulthood. ADHD causes difficulties in various area of life, such as academic achievement, peer relationships, family functioning, employment and marriage. Although ADHD is known to respond well to medication, such treatment is more effective when combined with psychosocial (non-pharmacologic) therapy in terms of alleviating the core symptoms and improving appropriate functions. Psychosocial treatment interventions are divided into psychoeducation, behavioral parent training, school intervention, cognitive behavior therapy, social skill training, parent-child interaction therapy, play therapy, other treatments (coaching, complementary and alternative medicine), neurofeedback and Cogmed. Adult ADHD cognitive behavioral therapy is described separately. These practice parameters summarize the evidence for psychosocial treatment. Based on this evidence, specific recommendations are provided for psychosocial interventions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.135-135
• /
• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• Journal of the Korean Data and Information Science Society
• /
• v.8 no.2
• /
• pp.163-171
• /
• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.9
• /
• pp.4087-4107
• /
• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.29 no.5
• /
• pp.466-472
• /
• 2009

An acoustic leak monitoring system(ALMS) using acoustic emission(AE) technique was applied for leakage detection of nuclear power plant's pipeline which is operated in high temperature and pressure condition. Since this system only monitors the existence of leak using the root mean square(RMS) value of raw signal from AE sensor, the difficulty occurs when the characteristics of leak size and shape need to be evaluated. In this study, dual monitoring system using AE sensor and accelerometer was introduced in order to solve this problem. In addition, artificial neural network(ANN) with Levenberg.Marquardt(LM) training algorithm was also applied due to rapid training rate and gave the reliable classification performance. The input parameters of this ANN were extracted from varying signal received from experimental conditions such as the fluid pressure inside pipe, the shape and size of the leak area. Additional experiments were also carried out and with different objective which is to study the generation and characteristic of lamb and surface wave according to the pipe thickness.