• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.1-8
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• 2023

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.

• Journal of Communications and Networks
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• v.5 no.1
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• pp.65-72
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• 2003

Internet roundtrip delay/time (RTT) prediction plays an important role in detecting packet losses in reliable transport protocols for traditional web applications and determining proper transmission rates in many rate-based TCP-friendly protocols for Internet-based real-time applications. The widely adopted autoregressive and moving average (ARMA) model with fixed-parameters is shown to be insufficient for all scenarios due to its intrinsic limitation that it filters out all high-frequency components of RTT dynamics. In this paper, we introduce a novel parameter-varying RTT model for Internet roundtrip time prediction based on the information theory and the maximum entropy principle (MEP). Since the coefficients of the proposed RTT model are updated dynamically, the model is adaptive and it tracks RTT dynamics rapidly. The results of our experiments show that the MEP algorithm works better than the ARMA method in both RTT prediction and RTO estimation.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.491-496
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• 2003

We present a new watermark design tool for digital images and digital videos that are based on human visual system (HVS) characteristics. In this tool, basic mechanisms (inhibitory and excitatory behaviour of cells) of HVS are used to determine image dependent upper bound values on watermark insertion. This allows us to insert maximai allowable transparent watermark, which in turn is extremely hard to attack with common image processing, Motion Picture Experts Group (MPEG) compression. As the number of details (e.g. edges) increases in an image, the HVS decrease its sensitivity to the details. In the same manner, as the number of motion increases in a video signal, the HVS decrease its sensitivity to the motions. We model this decreased sensitivity to the details and motions as an (motion) entropy masking. Entropy masking model can be efficiently used to increase the robustness of image and video watermarks. We have shown that our entropy-masking model provides watermark scheme with increased transparency and henceforth increased robustness.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.837-847
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• 2012

Inverse Gaussian distribution is widely used in applications to analyze and model right-skewed data. To assess the appropriateness of the distribution prior to data analysis, Mudholkar and Tian (2002) proposed an entropy-based test of fit. The test is based on the entropy power fraction(EPF) index suggested by Gokhale (1983). The simulation results report that the power of the entropy-based test is superior compared to other goodness-of-fit tests; however, this observation is based on the small-scale simulation results on the standard exponential, Weibull W(1; 2) and lognormal LN(0:5; 1) distributions. A large-scale simulation should be performed against various alternative distributions to evaluate the power of the entropy-based test; however, the use of a theoretical method is more effective to investigate the powers. In this paper, utilizing the information discrimination(ID) index defined by Ehsan et al. (1995) as a mathematical tool, we scrutinize the power of the entropy-based test. The selected alternative distributions are the gamma, Weibull and lognormal distributions, which are widely used in data analysis as an alternative to inverse Gaussian distribution. The study results are provided and an illustrative example is analyzed.

• Atmosphere
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• v.24 no.2
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• pp.141-150
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• 2014

This study investigates the characteristics of the Gross Moist Stability (GMS) over the tropics. The GMS summarizes the relationship between large-scale entropy forcing due to radiation and surface fluxes and the response of smaller-scale convection. The GMS is able to explain both to where moist entropy is advected by the atmospheric circulation and how deep the moisture flux convergence is in the tropical region. In the deep convective region, positive GMS appears over the warm pool region due to the strong column-integrated moisture convergence and the ensuing export of moist entropy to the environment. The vertical advection of moist entropy dominates over the horizontal advection in this region. Meanwhile, over the eastern tropical ITCZ region, which is characterized by shallow convective area, import of moist entropy by horizontal winds is dominant compared to the vertical moist entropy advection. Future changes in the GMS are also examined using the 22 CMIP5 model simulations. A decrease in the GMS appears widely across the tropics, but its increase occurs over the western-central equatorial Pacific. It is evident that the increased GMS region corresponds to an increased region of precipitation, implying that strengthened convection in the future due to increased entropy forcing exports the enhanced moist energy to stabilize the environment.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.201-212
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• 2017

This study presented the new evaluation index which can evaluate the discrimination of DEA models. To evaluate the discrimination of DEA models, data were analyzed using importance index as suggested in previous study and the coefficient of variation as suggested in this study for the discrimination evaluation. This study selected the CCR-DEA, BCC-DEA, entropy, bootstrap, super efficiency, and cross efficiency DEA model for the discrimination evaluation and accomplished empirical analysis. In order to grasp the rank correlation of the models, this study implemented the rank correlation analysis between the efficiency of CCR model and BCC model and entropy, bootstrap, super efficiency, and efficiency of the cross efficiency model. The obtained results of this study are as follows. First, the discrimination rank of models using the importance index and the coefficient of variation was shown to be identical. Therefore, the coefficient of variation can be used the discrimination evaluation index of DEA model. Second, the discrimination of the super efficiency model was found to be the highest rank among 4 models according to the analysis of this present study. Third, the highest rank correlation with CCR model was the super efficiency model. In addition, the super efficiency model was found to be the highest rank correlation with BCC model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.31-36
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• 2009

Bayesian networks are known as the best tools to express and predict the domain knowledge with uncertain environments. However, bayesian learning could be too difficult to do effective and reliable searching. To solve the problems of overtime demand, the nodes should be arranged orderly, so that effective structural learning can be possible. This paper suggests the classification learning model to reduce the errors in the independent condition, in which a lot of variables exist and data can increase the reliability by calculating the each entropy of probabilities depending on each circumstances. Also efficient learning models are suggested to decide the order of nodes, that has lowest entropy by calculating the numerical values of entropy of each node in K2 algorithm. Consequently the model of the most suitably settled Bayesian networks could be constructed as quickly as possible.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.972-980
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• 2022

The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.7-17
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• 2017

Loan consultants assist clients with loan application processing and loan decisions. Their duties may include contacting people to ask if they want a loan, meeting with loan applicants and explaining different loan options. We studied the efficiency of service quality of loan consultants contracted to a bank in Korea. They do not work as a team, but do work independently. Since he/she is not an employee of the bank, the consultant is paid solely in proportion to how much he/she sell loans. In this study, a consultant is considered as a decision making unit (DMU) in the DEA (Data Envelopment Analysis) model. We use a principal component analysis-data envelopment analysis (PCA-DEA) model integrated with Shannon's Entropy to evaluate quality efficiency of the consultants. We adopt a three-stage process to calculate the efficiency of service quality of the consultants. In the first stage, we use PCA to obtain 6 synthetic indicators, including 4 input indicators and 2 output indicators, from survey results in which questionnaire items are constructed on the basis of SERVQUAL model. In the second stage, 3 DEA models allowing negative values are used to calculate the relative efficiency of each DMU. In the third stage, the weight of each result is calculated on the basis of Shannon's Entropy theory, and then we generate a comprehensive efficiency score using it. An example illustrates the proposed process of evaluating the relative quality efficiency of the loan consultants and how to use the efficiency to improve the service quality of the consultants.