• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.74-79
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• 2008

The reinvestment assumption of the internal rate of return(IRR) method may not be valid in an engineering economy study. This situation, coupled with the computational demands and possible multiple interest rate associated with the IRR method, has given rise to other rate of return methods, such as the external rate of return(ERR) method, that can remedy some of these weaknesses. But ERRs are not used generally. We present another rate of return including all attributes of the minimum attractive rate of return(MARR).

• Health Policy and Management
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• v.6 no.2
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• pp.91-114
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• 1996

In this research, the Data Envelopment Analysis(DEA) was applied to measure production efficiency of Public Corporation Medical Centers(PCMCs) operating in Korea. The focus of this research is triple. First, identifing convenience and usefulness of DEA to measure the relative efficiency among PCMCs. Second, assessing magnitudes of the relative efficiency for each PCMC. Third, adding insights into some factors resulting inefficiency in PCMCs. Then, in this paper technical efficiency and scale efficiency measured by DEA[introduced by Charnes, Cooper, and Rhoides(1978) and Banker, Charnes, and Cooper(1984)] were analyzed and a new separate variable was introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decresing returns to scale(in multiple input and output situations). And a multi-factor Tobit analysis was conducted to see which variables are associated with PCMC's efficiency.

• Current Optics and Photonics
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• v.4 no.3
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• pp.186-192
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• 2020

This paper describes a real-time monitoring system in Ethernet passive optical networks (EPON) that uses burst-mode fiber Bragg grating (FBG) optical sensors. The FBG interrogation unit in the optical line terminal (OLT) transmits the monitoring wavelength to optical network units (ONUs). The FBG sensor unit in each ONU returns a burst-mode monitoring signal to the OLT. As the system applies time division multiple access (TDMA), a uniform Bragg wavelength can be used to monitor the EPON system. The FBG interrogation unit analyzes the received burst-mode monitoring signals and outputs fault information on the ONU branches in EPON. The simulation results show the effectiveness of the proposed monitoring system based on TDMA. In addition, we compared the proposed TDMA-based monitoring system with a WDMA-based monitoring system.

• ETRI Journal
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• v.16 no.1
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• pp.17-34
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• 1994

In this paper, we present an architecture of the constant false alarm rate (CFAR) detector called the generalized order statistics (GOS) CFAR detector, which covers various order statistics (OS) and cell-averaging (CA) CFAR detectors as special cases. For the proposed GOS CFAR detector, we obtain unified formulas for the false alarm and detection probabilities. By properly choosing coefficients of the GOS CFAR detector, one can utilize any combination of ordered samples to estimate the background noise level. Thus, if we use a reference window of size N, we can realize $(2^N-1)$ kinds of CFAR processors and obtain their performances from the unified formulas. Some examples are the CA, the OS, the censored mean level, and the trimmed mean CFAR detectors. As an application of the GOS CFAR detector to multiple target detection, we propose an algorithm called the adaptive mean level detector, which censors adaptively the interfering target returns in a reference window.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.280-286
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• 2022

Facial recognition system is a biometric manipulation. Its applicability is simpler, and its work range is broader than fingerprints, iris scans, signatures, etc. The system utilizes two technologies, such as face detection and recognition. This study aims to develop a facial recognition system to recognize person's faces. Facial recognition system can map facial characteristics from photos or videos and compare the information with a given facial database to find a match, which helps identify a face. The proposed system can assist in face recognition. The developed system records several images, processes recorded images, checks for any match in the database, and returns the result. The developed technology can recognize multiple faces in live recordings.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.123-135
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• 2016

This study uses Data Envelopment Analysis(DEA) to investigate the management efficiency of Korean shipping companies based on business administration costs such as corporate entertainment, advertisement, and labor costs. We analyze shipping enterprises listed on the Korean stock market of the period of 2010-2014. Corporate entertainment, advertisement and labor costs are used as input variables and sales and net income are used as output variables. We use technical efficiency, pure technical efficiency, scale efficiency and returns to scale to propose a plan to improve the efficiency of inefficiency decision-making units (DMUs). The results of the efficiency analysis show that six of the DMUs in the technical efficiency of CCR model and eight of the DMUs in the pure technical efficiency of BCC model are in efficient state. In terms of return to scale, six of the DMUs(24% of all DMUs) show increasing returns to scale, while 13 DMUs(52% of all DMUs) showdecreasing returns to scale. Because multiple efficient state for DMUs exist in the technical efficiency analysis, we conduct a super efficiency analysis. The results show that the efficient state of the twomost efficient DMUs are 1.314 and 1.243, respectively. This implies that these DMUs could maintain their current levels of the efficiency if they increase the amount spent on advertisements, corporate entertainment and labor costs by 31.4% and 24.3%. respectively. We conclude this study by providing the efficiency states of each DMU and target for improving the inefficiencies in each case.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.95-99
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• 2015

The IRR(internal rate of return) is often used by investors for the evaluation of engineering projects. Unfortunately, it has serial flaws: (1) multiple real-valued IRRs may arise; (2) complex-valued IRRs may arise; (3) the IRR is, in special cases, incompatible with the net present value (NPV) in accept/reject decisions. The efforts of management scientists and economists in providing a reliable project rate of return have generated over the decades an immense amount of contributions aiming to solve these shortcomings. Especially, multiple internal rate of returns (IRRs) have a fatal flaw when we decide to accep it or not. To solve it, some researchers came up with external rate of returns (ERRs) such as ARR (Average Rate of Return) or MIRR (MIRR, Modified Internal Rate of Return). ARR or MIRR. will also always yield the same decision for a engineering project consistent with the NPV criterion. The ERRs are to modify the procedure for computing the rate of return by making explicit and consistent assumptions about the interest rate at which intermediate receipts from projects may be invested. This reinvestment could be either in other projects or in the outside market. However, when we use traditional ERRs, a volume of capital investment is still unclear. Alternatively, the productive rate of return (PRR) can settle these problems. Generally, a rate of return is a profit on an investment over a period of time, expressed as a proportion of the original investment. The time period is typically the life of a project. The PRR is based on the full life of the engineering project. but has been annualised to project one year. And the PRR uses the effective investment instead of the original investment. This method requires that the cash flow of an engineering project must be separated into 'investment' and 'loss' to calculate the PRR value. In this paper, we proposed a tabulated form for easy calculation of the PRR by modifing the profit and loss statement, and the cash flow statement.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.29-39
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• 2018

In this paper, we discuss a discrete-time queueing system with dyadic server control policy that combines workload control and multiple vacations. Customers arrive at the system with Bernoulli arrival process. If there is no customer to serve in the system, an idle single server spends a vacation of discrete random variable V and returns. The server repeats the vacation until the total service time of waiting customers exceeds the predetermined workload threshold D. In this paper, we derived the steady-state workload distribution of a discrete-time queueing system which is operating under a more realistic and flexible server control policy. Mean workload is also derived as a performance measure. The results are basis for the analysis of system performance measures such as queue lengths, waiting time, and sojourn time.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.749-759
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• 2020

Over the years, numerous Micro, Small, and Medium Enterprises (MSMEs) have been vigorously established across many countries. Even though the Internet of Things (IoT) has enabled companies to anchorage business returns, most Indonesian MSMEs are highly susceptible to failure and one of the main issues is the inability to manage their financials effectively. The literature on accounting points out that the success of MSMEs owing to the usage of cloud-based Accounting Information Systems (AIS) or Cloud Accounting (CA) could reduce the rate of failure by managing multiple accounting information at a low cost. Although many benefits exist, Indonesian MSMEs are not adopting these platforms in their daily business activities. This study investigates the factors that influence Indonesian MSMEs' intention to adopt CA. The study is directed by unstructured in-depth interviews with seven bestseller MSMEs where a thematic analysis technique was employed to identify them. The interview findings and prevailing literature on the influencing factors based on the TOE (technological, organizational, and environmental) framework to adopt CA in Indonesian MSMEs context are perceived benefits outweighing the cost, perceived compatibility, perceived complexity, owner-manager knowledge on accounting, organization size, competitive pressure, and informal network. The conceptual model further includes government intervention as a moderator in the model.