• The Transactions of the Korean Institute of Electrical Engineers A
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• 제50권9호
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• pp.399-399
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• 제50권9호
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• pp.309-405
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• IE interfaces
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• 제8권2호
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• pp.161-169
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• 1995

In this paper, a threshold policy is considered for the Markovian queueing system with server vacations. The threshold policy considered in this paper has the following form: "when the number of customers present in the system increases to N, the server is turned on and serves customers until the system becomes empty". In this paper, we show how the finite capacity or finite population queueing system under a threshold policy can be modeled by the stochastic Petri net. The performance evaluation of the model is carried out using the software called "SPNP". Some examples are also presented in which it is shown that how the optimal threshold policies can be obtained under a linear cost structure.

• Safety and Health at Work
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• 제8권2호
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• pp.206-211
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• 2017

Background: Self-reported low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting nonchemical stressors and related adverse responses is more difficult. Stressor-response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g., Benchmark Dose methods), so different approaches were tried. Methods: This work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function. Results: Using maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found. Conclusion: A risk of LBP associated with CLI values > 1.5 existed in this worker population. The relevance for other populations requires further study.

• Bulletin of the Korean Mathematical Society
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• 제43권4호
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• pp.813-820
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• 2006

We consider a MAR model with ARCH type conditional heteroscedasticity. MAR-ARCH model can be derived as a smoothed version of the double threshold AR-ARCH model by adding a random error to the threshold parameters. Easy to check sufficient conditions for strict stationarity, ${\beta}-mixing$ property and existence of moments of the model are given via Markovian representation technique.

• Communications for Statistical Applications and Methods
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• 제17권2호
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• pp.255-261
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• 2010

A class of threshold-asymmetric GRACH(TGARCH, hereafter) models has been useful for explaining asymmetric volatilities in the field of financial time series. The cumulative impulse response function of a conditionally heteroscedastic time series often measures a degree of unstability in volatilities. In this article, a general form of the cumulative impulse response function of the TGARCH model is discussed. In particular, We present formula in their closed forms for the first two lower order models, viz., TGARCH(1, 1) and TGARCH(2, 2).

• IEMEK Journal of Embedded Systems and Applications
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• 제18권2호
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• pp.59-65
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• 2023

Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

• Asian-Australasian Journal of Animal Sciences
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• 제18권12호
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• pp.1669-1674
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• 2005

Maximum likelihood methodology was applied to analyze the efficiency and statistical power of interval mapping by using a threshold model. The factors that affect QTL detection efficiency (e.g. QTL effect, heritability and incidence of categories) were simulated in our study. Daughter design with multiple families was applied, and the size of segregating population is 500. The results showed that the threshold model has a great advantage in parameters estimation and power of QTL mapping, and has nice efficiency and accuracy for discrete traits. In addition, the accuracy and power of QTL mapping depended on the effect of putative quantitative trait loci, the value of heritability and incidence directly. With the increase of QTL effect, heritability and incidence of categories, the accuracy and power of QTL mapping improved correspondingly.

• Proceedings of the KIEE Conference
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.184-186
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• 2000

This paper proposes the introduction of TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. TAR model is a piecewise linear autoregressive model. In the scatter diagram of daily peak load versus daily maximum or minimum temperature, we can find out that the load-temperature relationship has a negative slope in lower regime and a positive slope in upper regime due to the heating and cooling load, respectively. In this paper, daily peak load was forecasted by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.