• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1102-1105
• /
• 1993

A fuzzy expert system for prediction and mitigation of sludge bulking was developed for an activated sludge process which treats waste water from a food industry. The developed system is able not only to infer the degree of progress of sludge bulking but also to generate remedial operation guides which may be sent to the local controllers as remote set points. One of the important consequences through this study is the BI (Bulking Index) inferred by the bulking prediction expert system was found to have a close correlation with the SVI (Sludge Volume Index) which is a practical measure of degree of bulking but needs tedious chores for its measurement.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.4
• /
• pp.633-636
• /
• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• Computers and Concrete
• /
• v.24 no.6
• /
• pp.555-560
• /
• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.13-22
• /
• 2004

Because video sequence consists of dynamic objects in nature, the object motion in video is an effective feature in describing the contents of video sequence and motion feature plays an important role in video retrieval. In this paper, we propose a method that converts motion vectors (MVs) to a uniform set on MPEG coded domain, independent of the frame type and the direction of prediction, and utilizes these normalized MVs (N-MVs) as motion descriptor to understand video contents. We describe a frame-type independent representation of the various types of frames presented in an MPEG video in which all frames can be considered equivalently, without full-decoding. In the experiments, we show that the proposed method is better than the conventional one in terms of performance.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.131-131
• /
• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• Journal of Power System Engineering
• /
• v.11 no.2
• /
• pp.38-43
• /
• 2007

전자기적으로 지지되는 임펠러를 가진 원심 혈액 펌프는 기존의 심장 펌프에 비해 많은 장점을 가지고 있지만, BVAD의 틈새에서 발생하는 유체 동역학적인 문제는 여전히 규명이 되지 않은 상태이다. 본 연구에서는 BVAD의 틈새에서 발생하는 혈액외상(blood trauma)의 예측에 대한 연구에 중점을 두고 있다. 일반적으로 원심 혈액 펌프의 설계를 위해 전자기적으로 지지되는 원심 혈액 펌프의 디스크 틈새에서 발생하는 혈액의 손상을 평가하는 방법으로 CFD를 이용한 방법이 널리 이용되고 있다. 따라서, 본 연구에서는 초기 원심 혈액 펌프의 설계 단계에서 펌프의 특성을 평가하기 위하여, 축 방향 틈새의 영향과 회전수 변화에 따른 누수경로의 전단 응력의 크기 평가를 CFD를 사용하여 해석하여 보았다.

• Journal of Information Processing Systems
• /
• v.8 no.4
• /
• pp.685-692
• /
• 2012

B frame bi-directional predictions and the DIRECT mode coding of the H.264 video compression standard necessitate a complex mode decision process, resulting in a long computation time. To make H.264 feasible, this paper proposes an image backtrack-based fast (IBFD) algorithm and evaluates the performances of two promising fast algorithms (i.e., AFDM and IBFD). Evaluation results show that an image backtrack-based fast (IBFD) algorithm can determine DIRECT mode macroblocks with 13% higher accuracy, as compared with the AFDM. Furthermore, IBFD is shown to reduce the motion estimation time of B frames by up to 23% with a negligible quality degradation.

• Vacuum Magazine
• /
• v.1 no.4
• /
• pp.21-24
• /
• 2014

Thermolectric devices could convert temperature gradient into electricity (Seebeck effect) and electric power into temperature gradient across the themoelectric element (Peltier effect). $Bi_2Te_3$ has been widely used as thermoelectric material for more than 40 years, due to the superior thermoelctric characteristics. However, Bi and Te materials are predicted to face supply shortage, giving strong necessity for the development of new thermoelctric materials. Based on the theoretical prediction, nanostructure are expected to give dramatic enhnacement of thermoelectirc characteristics by controlling phonon propagation. Thus, silicon, which had been considered as improper material for thermoelectricity, is now being considered as strong cadidate material for thermoelectricity. This review will focus on the nanotechnology applied research activities in silicon as thermoelectric materials.

• Structural Engineering and Mechanics
• /
• v.89 no.6
• /
• pp.571-578
• /
• 2024

The bistable thin cylindrical shell is developable structure with the ability to transition between its two stable configurations. This structure offers significant potential applications due to its excellent deformability. In this paper, the composite thin cylindrical shell consisting of the composite layer and the piezoelectric layer was investigated. The material and geometric parameters of the shell were found to influence its stable characteristics. The analysis model of the composite thin cylindrical shell incorporating the piezoelectric layer was developed, and the expressions for its strain energy were derived. By applying the minimum energy principle, the impact of the electric field intensity on the bi-stable behaviors of the cylindrical shell was analyzed. The results showed that the shell exhibited the bistability only under the appropriate electric field strength. And the accuracy of the theoretical prediction was verified by simulation experiments. This study provides an important reference for the application of deployable structures.

• Structural Engineering and Mechanics
• /
• v.32 no.3
• /
• pp.459-475
• /
• 2009

This paper presents analytical methodologies for remaining life prediction of plain concrete structural components considering tension softening and size effects. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. Size effect has been accounted for by modifying the Paris law, leading to a size adjusted Paris law, which gives crack length increment per cycle as a power function of the amplitude of a size adjusted stress intensity factor (SIF). Details of tension softening effects and size effect in the computation of SIF and remaining life prediction have been presented. Numerical studies have been conducted on three point bending concrete beams under constant amplitude loading. The predicted remaining life values with the combination of tension softening & size effects are in close agreement with the corresponding experimental values available in the literature for all the tension softening models.