• Quality Improvement in Health Care
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• v.2 no.2
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• pp.112-129
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• 1996

Background : A small number of high cost patients usually spend a larger proportion of scarce health resources. Aged, long-term care and readmitted patients usually belong to these high cost patient group. Among others, long length of stay and readmission can be reduced by checking its cause, and these are the areas needed most of quality improvement activity. Characteristics of high cost medical users between health insurance program and medical assistance program were reviewed. Methods : The inpatient claims of health insurance and medical assistance program were analyzed. Patients were divided by 6 groups; long-term, mid-term, short-term, readmitted, cancer and aged. We defined high cost patients as those who had spent one and half million won and over per 6 months. Characteristics of high cost patients for each group were reviewed. Results : medical assistance patients used much more resources than the insured members in the average hospital cost per case but less in daily hospital cost. The former had a longer length of stay and had much heavier diseases. Major diseases of both group were cancer, diseases of circulatory system and chronic degenerative diseases. Gallstone and schizophrenia were more in the insured program. However, pulmonary tuberculosis, asthma were more common among the medical assistance patients. Early readmission before 2 weeks were 28-30% of the total readmission. Readmission rate in the malignat neoplasm and renal failure were 80% and more. Q.A program should be installed to prevent unnecessary readmissions. Conclusion : Almost 30% of early readmissions and admissions due to complications and long length of stay should be reviewed carefully to keep cost down and to enhance the quality of hospital care.

• Wind and Structures
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• v.26 no.2
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• pp.69-74
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• 2018

Nowadays, using math logic in great civil projects is considered by the clients to achieve the goals of project including quality optimization, costs, avoiding individual, emotional and political decision making, long-term and short-term goals and they are the main requirements of each project and should be considered by the decision makers to avoid the illogical decision making applied on the majority of civil projects and this imposes great financial and spiritual costs on our country. The present study attempts to present one of the civil projects (Ghasre Shirin storage dam) whose client was not ministry of energy for the first time and the short-term and long-term goals of the private sector were applied based on the triangle of quality, cost and time. Also, the math logic and model (multi-criteria decision making method and decision making matrix) is used in one of the most important sections of project, sealing element, policies and new materials (Geosynthetics) are considered and this leads to suitable decision making in this regard. It is worth to mention that this method is used for other sections of a dam including body, water diversion system, diaphragm and other sectors or in other civil projects of building, road construction, etc.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.51-61
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• 2020

This research investigates the consequences of the increase in corporate use of short-term debt in China over the past decades. Using a sample of Chinese firms from 2007 to 2018, we empirically explore the effect of corporate use of short-term debt for long-term investment (SFLI) on audit pricing. We first examine the relationship between SFLI and audit pricing for different groups of firms. Then, we investigate the role of the increase in short-term debt in alleviating principal-agent conflicts and reducing agency costs. We have four primary empirical findings. First, auditors tend to charge SFLI clients higher fees. Second, the negative relationship between SFLI and audit fee is found in private firms, firms audited by Chinese domestic auditors, and firms with higher information asymmetry. Third, the time auditors spent on SFLI clients is significantly more than that spent on non-SFLI clients, suggesting that the decrease in audit fee is not due to the decrease in cost. Fourth, SFLI significantly reduces the agency costs of the firm, which auditors regard as a low risk signal and grant an audit fee discount. Our findings suggest that the decrease in debt maturity, not only influences managerial behaviors, but also influences auditors' risk assessment and pricing decisions.

• International Journal of Contents
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• v.15 no.4
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• pp.8-15
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• 2019

This paper presents a solution of the 'Quick, Draw! Doodle Recognition Challenge' hosted by Google. Doodles are drawings comprised of concrete representational meaning or abstract lines creatively expressed by individuals. In this challenge, a doodle is presented as a sequence of sketches. From the view of at the sketch level, to learn the pattern of strokes representing a doodle, we propose a sequential model stacked with multiple convolution layers and Long Short-Term Memory (LSTM) cells following the attention mechanism [15]. From the view at the image level, we use multiple models pre-trained on ImageNet to recognize the doodle. Finally, an ensemble and a post-processing method using the minimum cost flow algorithm are introduced to combine multiple models in achieving better results. In this challenge, our solutions garnered 11th place among 1,316 teams. Our performance was 0.95037 MAP@3, only 0.4% lower than the winner. It demonstrates that our method is very competitive. The source code for this competition is published at: https://github.com/ngxbac/Kaggle-QuickDraw.

• The Journal of Bigdata
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• v.4 no.2
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• pp.35-45
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• 2019

Recently, the damage with social cost of malicious comments is increasing. In addition to the news of talent committing suicide through the effects of malicious comments. The damage to malicious comments including abusive language and slang is increasing and spreading in various type and forms throughout society. In this paper, we propose a technique for detecting abusive language using a bi-directional long short-term memory neural network model. We collected comments on the web through the web crawler and processed the stopwords on unused words such as English Alphabet or special characters. For the stopwords processed comments, the bidirectional long short-term memory neural network model considering the front word and back word of sentences was used to determine and detect abusive language. In order to use the bi-directional long short-term memory neural network, the detected comments were subjected to morphological analysis and vectorization, and each word was labeled with abusive language. Experimental results showed a performance of 88.79% for a total of 9,288 comments screened and collected.

• IE interfaces
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• v.15 no.3
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• pp.279-285
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• 2002

A break-even analysis is a method used widely for profit planning or decisions in most companies. It is useful tool in financial studies because it is simple and offers useful insights from a modest amount of data. Although it is widely used, it has some weaknesses. It is limited in particular to the analysis for a short term time horizon or one period. We suggest a new break-even procedure to analyze projects with a long term time horizon as keeping the simplicity of a conventional break-even analysis. We will make efforts doing to include actual data for a cost or an income as much as possible rather than developing a mathematical model to improve unreality of a traditional break-even analysis. Also, we will use the spreadsheet software to solve problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.97-105
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• 2011

Previous researches have focused on the efficiency of project execution and the satisfaction of internal customers In view of the fact that a project is successful if any defects are not found in the short-term performance test of the project final outcome. To execute a project that both internal customer and external customer are satisfied in terms of longer-term benefit perspective, the project claim costs (PCC) which may occur for the warranty period of the project final outcome should be considered. We propose a model included PCC to the linear programming between time and cost to expedite a project and perform the validity test by applying the model to an example project. This model and related procedure will contribute to overall project activities' cost reduction by taking preventive actions for PCC.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.107-122
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• 2020

Purpose - This paper investigates the relations among exploratory and exploitative supply chain management practices, competitive advantage, and firm performance. Design/methodology/approach - This study takes a hypothesis-generating study to capture the tradeoffs between exploration and exploitation and develops some hypotheses which involve the relations among SCM practices, competitive advantage, and short-term and long-term performance. Findings - Exploitative SCM practices have more positive effects on short-term performance rather than long-term performance, whereas exploratory SCM activities affect long-term performance. Further competitive strategy, environmental dynamism, and organizational slack moderates the relationship between SCM and performance. Exploitative SCM is more desirable when a firm uses low cost strategy, lacks slack resources, and faces stable environment, while exploratory SCM is more effective when a firm employs differentiation strategy, has slack resources, and confront dynamic environment. Research implications or Originality - In order to understand the performance effects of a variety of SCM practices, we should distinguish between exploitative and exploratory SCM activities. Further the relationship between SCM and performance may differ depending upon some contingent variables such as external environment, competitive strategy and organizational slack.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1882-1890
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• 2014

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of $NO_x$, $SO_2$, and $CO_2$ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.