A stochastic model of the internal control system / BEBR No. 106

Typescript.Includes bibliographical references (leaf [30])

The theoretical structure of audit opinion space / 194

Includes bibliographical references

The experience of Korea electronics industry

노트 : Seminar material for KDI/EDI Seminar on Korea's Experience in Trade and Industry Development: Its Relevance for Latin Ameria, Seoul, Korea, November 24 - December 2, 1986. 행사명 : Korea's Experience in Trade and Industry Developmen

The development of the Korean electronics industry with special reference to Samsung Electronics

노트 : Presented at International Forum on Industrial Planning and Trade Policies, Seoul, Kores, June 1-12, 1982 행사명 : International Forum on Industrial Planning and Trade Policie

Korea's foreign investment in developing countries

노트 : Presented at the Korea Development Institute, Policy Forum on "Foreign Direct Investment and Economic Development of LDCs", Seoul, Korea, October 16-20, 1989 행사명 : Policy Forum on Foreign Direct Investment and Economic Development of LDC

The Design of an Intelligent Lightweight Stock Trading System Using Deep Learning Models: Employing Technical Analysis Methods

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in this study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we assessed the performance of eight deep learning models: long short-term memory (LSTM), a convolutional neural network (CNN), bidirectional LSTM (BiLSTM), CNN Attention, a bidirectional gated recurrent unit (BiGRU) CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market

A Simple Analysis Method of Specific Anammox Activity Using a Respirometer

Anaerobic ammonium oxidation (anammox) is a biological nitrogen removal process with attractive prospects, such as no carbon addition, less aeration, lower greenhouse gas generation, and lower sludge production. However, it is difficult to maintain a stable anammox process since the anammox bacteria have a slow growth rate and high sensitivity to many factors. Therefore, it is very important to analyze and maintain the anammox activity as a process indicator for its successful operation. The conventional method for measuring the concentration of nitrogen compounds, such as ammonium, nitrite, or nitrogen gas is inconvenient during the reaction time for specific anammox activity (SAA) analysis, which can result in an inaccurately determined SAA due to the substrate loss and temperature change. In this study, a respirometer was utilized to analyze the SAA. The SAA values from a respirometer (rSAA) showed a similar pattern to the SAA values (mSAA) from the conventional method. All of the SAA analyses showed the highest value at 35 °C with a granule size of <1 mm. Statistical analysis showed no significant differences regardless of the analysis method, since the p-values for the t-test and Wilcoxon rank-sum test were >0.05. Therefore, the respirometer can be used as a simple and efficient tool for SAA analysis. Moreover, the operating maintenance and management of the anammox process can be improved due to the simple SAA analysis in the field