• Title/Summary/Keyword: predict intervals

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The analysis of Photovoltaic Power using Terrain Data based on LiDAR Surveying and Weather Data Measurement System (LiDAR 측량 기반의 지형자료와 기상 데이터 관측시스템을 이용한 태양광 발전량 분석)

  • Lee, Geun-Sang;Lee, Jong-Jo
    • Journal of Cadastre & Land InformatiX
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    • v.49 no.1
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    • pp.17-27
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    • 2019
  • In this study, we conducted a study to predict the photovoltaic power by constructing the sensor based meteorological data observation system and the accurate terrain data obtained by using LiDAR surveying. The average sunshine hours in 2018 is 4.53 hours and the photovoltaic power is 2,305 MWh. In order to analyze the effect of photovoltaic power on the installation angle of solar modules, we installed module installation angle at $10^{\circ}$ intervals. As a result, the generation time was 4.24 hours at the module arrangement angle of $30^{\circ}$, and the daily power generation and the monthly power generation were the highest, 3.37 MWh and 102.47 MWh, respectively. Therefore, when the module arrangement angle is set to $30^{\circ}$, the generation efficiency is increased by about 4.8% compared with the module angle of $50^{\circ}$. As a result of analyzing the influence of the seasonal photovoltaic power by the installation angle of the solar module, it was found that the photovoltaic power was high in the range of $40^{\circ}{\sim}50^{\circ}$, where the module angle was large from November to February when the weather was cold. From March to October, it was found that the photovoltaic power amount is $10^{\circ}{\sim}30^{\circ}$ with small module angle.

Financial Market Prediction and Improving the Performance Based on Large-scale Exogenous Variables and Deep Neural Networks (대규모 외생 변수 및 Deep Neural Network 기반 금융 시장 예측 및 성능 향상)

  • Cheon, Sung Gil;Lee, Ju Hong;Choi, Bum Ghi;Song, Jae Won
    • Smart Media Journal
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    • v.9 no.4
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    • pp.26-35
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    • 2020
  • Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.

Correlation Between Accompanying Symptoms of Facial Nerve Palsy, Clinical Assessment Scales and Surface Electromyography

  • Gyu Hui, Kim;Jung Hyeon, Park;Tae Kyung, Kim;Eun Ju, Lee;Su Eun, Jung;Jong Cheol, Seo;Cheol Hong, Kim;Yoo Min, Choi;Hyun Min, Yoon
    • Journal of Acupuncture Research
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    • v.39 no.4
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    • pp.297-303
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    • 2022
  • Background: This retrospective study aimed to determine whether there were correlations between the number and type of accompanying symptoms of peripheral facial nerve palsy, and surface electromyography (SEMG) and clinical assessment scales to help diagnosis. Methods: There were 30, cases of peripheral facial nerve palsy at Visit 1 to the Korean Medicine Hospital, Dong-eui University, 22 cases at Visit 2 and 10 cases at Visit 3. The study period was from July 19, 2021 to November 31, 2021. Symptoms were evaluated three times (with two-week intervals which began 7 days from onset) using SEMG, clinical assessment scales and accompanying symptoms. In this study, the House-Brackmann grading system (HBGS), and the Yanagihara's unweighted grading system (Y-score) clinical assessment scales were used. The Pearson or Spearman correlation was used for statistical analysis. Results: On Visit 1, the number of accompanying symptoms of peripheral facial nerve palsy had no significant correlation with other measures. On Visits 1-3, the HBGS score had a significant negative correlation with the Y-score. On Visit 2, most of the mean values measured had significant correlations with each other although not between SEMG-Z and SEMG-O that Z means a zygomaticus muscle and O means a orbicularis oris muscle. On Visit 3, the number of accompanying symptoms significantly correlated with the clinical assessment scales. The HBGS score, Y-score, and SEMG measurements (except SEMG-Z) had significant correlations with each other. A significant positive correlation between SEMG-Z and SEMG-T was noted. Conclusion: We predict accompanying symptoms can be used to diagnose the peripheral facial nerve palsy including both clinical assessment scales and SEMG measurements at 2-5 weeks after onset.

Effects of Water Microspraying, Foliar Application of Calcium Carbonate and Kaolin Solution on Sunburn and Quality of Fruits in Satusma Mandarin (미세살수와 탄산칼슘 및 카올린 엽면살포가 온주밀감의 일소 발생과 과실 품질에 미치는 영향)

  • Joa, Jae-Ho;Kang, Seok-Beom;Park, Yo-Sup;Kim, Mi-Sun;Kim, Ha-Neul;Oh, Bok-Shim
    • Journal of Bio-Environment Control
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    • v.31 no.4
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    • pp.261-269
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    • 2022
  • This study was carried out to predict fruit surface temperature (FST) causing sunburn symptoms and assess the correlation between solar radiation and FST in satusma mandarin (Citrus unshiu Marc. 'Iwasaki'). And to reduce sunburn when ambient temperature was at 31℃, sprinkler system was repeated spraying every at 5 minutes of 30 minutes intervals and 1% calcium carbonate and 4% kaolin solution applied on citrus tree three times to investigate quality and sunburn of fruits. It showed highly significant positive correlation between FST and solar radiation (R = 0.788, p < 0.01), The FST increased up to 16℃ than ambient temperature at daytime. To estimate FST, a linear model y = 0.099 × (air temperature) + 0.018 × (solar radiation) + 20.779 (R = 0.687, p < 0.01) was derived. The canopy temperature of citrus trees was 5.1℃ lower in sprinkler system than in control. The incidence of sunburn showed at 2.1% in sprinkler system twice lower than in control and there was different significance. Fruit quality was similar between treatments. In conclusion, sunburn can be reduced by reflecting sunlight or downing of FST through sprinkler system.

THE USE OF NEAR INFRARED REFLECTANCE SPECTROSCOPY(NIRS) TO PREDICT CHEMICAL COMPOSITION ON MAIZE SILAGE

  • D.Cozzolino;Fassio, A.;Mieres, J.;Y.Acosta
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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    • 2001.06a
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    • pp.1610-1610
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    • 2001
  • Microbiological examination of silage is of little value in gauging the outcome of silage, and so chemical analysis is more reliable and meaningful indicator of quality. On the other hand chemical assessments of the principal fermentation products provide an unequivocal basis on which to judge quality. Livestock require energy, protein, minerals and vitamins from their food. While fresh forages provide these essential items, conserved forages on the other hand may be deficient in one or more of them. The aim of the conservation process is to preserve as many of the original nutrients as possible, particularly energy and protein components (Woolford, 1984). Silage fermentation is important to preservation of forage with respect of feeding value and animal performance. Chemical and bacteriological changes in the silo during the fermentation process can affect adversely nutrient yield and quality (Moe and Carr, 1984). Many of the important chemical components of silage must be assayed in fresh or by extraction of the fresh material, since drying either by heat or lyophilisation, volatilises components such as acids or nitrogenous components, or effects conversion to other compounds (Abrams et al., 1987). Maize silage dorms the basis of winter rations for the vast majority of dairy and beef cattle production in Uruguay. Since nutrient intake, particularly energy, from forages is influenced by both voluntary dry matter intake and digestibility; there is a need for a rapid technique for predicting these parameters in farm advisory systems. Near Infrared Reflectance Spectroscopy (NIRS) is increasingly used as a rapid, accurate method of evaluating chemical constituents in cereals and dried forages. For many years NIRS was applied to assess chemical composition in dry materials (Norris et al., 1976, Flinn et al., 1992; Murray, 1993, De Boever et al., 1996, De la Roza et al., 1998). The objectives of this study were (1) to determine the potential of NIRS to assess the chemical composition of dried maize samples and (2) to attempt calibrations on undried samples either for farm advisory systems or for animal nutrition research purposes in Uruguay. NIRS were used to assess the chemical composition of whole - plant maize silage samples (Zea mays, L). A representative population of samples (n = 350) covering a wide distribution in chemical characteristics were used. Samples were scanned at 2 nm intervals over the wavelength range 400-2500 nm in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance mode. Cross validation was used to avoid overfitting of the equations. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The calibration statistics were R$^2$ 0. 86 (SECV: 11.4), 0.90 (SECV: 5.7), 0.90 (SECV: 16.9) for dry matter (DM), crude protein (CP), acid detergent fiber (ADF) in g kg$\^$-1/ on dry matter, respectively for maize silage samples. This work demonstrates the potential of NIRS to analyse whole - maize silage in a wide range of chemical characteristics for both advisory farm and nutritive evaluation.

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An Approach Using LSTM Model to Forecasting Customer Congestion Based on Indoor Human Tracking (실내 사람 위치 추적 기반 LSTM 모델을 이용한 고객 혼잡 예측 연구)

  • Hee-ju Chae;Kyeong-heon Kwak;Da-yeon Lee;Eunkyung Kim
    • Journal of the Korea Society for Simulation
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    • v.32 no.3
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    • pp.43-53
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    • 2023
  • In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.

Classification of nasal places of articulation based on the spectra of adjacent vowels (모음 스펙트럼에 기반한 전후 비자음 조음위치 판별)

  • Jihyeon Yun;Cheoljae Seong
    • Phonetics and Speech Sciences
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    • v.15 no.1
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    • pp.25-34
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    • 2023
  • This study examined the utility of the acoustic features of vowels as cues for the place of articulation of Korean nasal consonants. In the acoustic analysis, spectral and temporal parameters were measured at the 25%, 50%, and 75% time points in the vowels neighboring nasal consonants in samples extracted from a spontaneous Korean speech corpus. Using these measurements, linear discriminant analyses were performed and classification accuracies for the nasal place of articulation were estimated. The analyses were applied separately for vowels following and preceding a nasal consonant to compare the effects of progressive and regressive coarticulation in terms of place of articulation. The classification accuracies ranged between approximately 50% and 60%, implying that acoustic measurements of vowel intervals alone are not sufficient to predict or classify the place of articulation of adjacent nasal consonants. However, given that these results were obtained for measurements at the temporal midpoint of vowels, where they are expected to be the least influenced by coarticulation, the present results also suggest the potential of utilizing acoustic measurements of vowels to improve the recognition accuracy of nasal place. Moreover, the classification accuracy for nasal place was higher for vowels preceding the nasal sounds, suggesting the possibility of higher anticipatory coarticulation reflecting the nasal place.

Comparison of the Characteristics between the Dynamical Model and the Artificial Intelligence Model of the Lorenz System (Lorenz 시스템의 역학 모델과 자료기반 인공지능 모델의 특성 비교)

  • YOUNG HO KIM;NAKYOUNG IM;MIN WOO KIM;JAE HEE JEONG;EUN SEO JEONG
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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    • v.28 no.4
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    • pp.133-142
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    • 2023
  • In this paper, we built a data-driven artificial intelligence model using RNN-LSTM (Recurrent Neural Networks-Long Short-Term Memory) to predict the Lorenz system, and examined the possibility of whether this model can replace chaotic dynamic models. We confirmed that the data-driven model reflects the chaotic nature of the Lorenz system, where a small error in the initial conditions produces fundamentally different results, and the system moves around two stable poles, repeating the transition process, the characteristic of "deterministic non-periodic flow", and simulates the bifurcation phenomenon. We also demonstrated the advantage of adjusting integration time intervals to reduce computational resources in data-driven models. Thus, we anticipate expanding the applicability of data-driven artificial intelligence models through future research on refining data-driven models and data assimilation techniques for data-driven models.

Mathematical Transformation Influencing Accuracy of Near Infrared Spectroscopy (NIRS) Calibrations for the Prediction of Chemical Composition and Fermentation Parameters in Corn Silage (수 처리 방법이 근적외선분광법을 이용한 옥수수 사일리지의 화학적 조성분 및 발효품질의 예측 정확성에 미치는 영향)

  • Park, Hyung-Soo;Kim, Ji-Hye;Choi, Ki-Choon;Kim, Hyeon-Seop
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.36 no.1
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    • pp.50-57
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    • 2016
  • This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

Evaluation of Moisture and Feed Values for Winter Annual Forage Crops Using Near Infrared Reflectance Spectroscopy (근적외선분광법을 이용한 동계사료작물 풀 사료의 수분함량 및 사료가치 평가)

  • Kim, Ji Hea;Lee, Ki Won;Oh, Mirae;Choi, Ki Choon;Yang, Seung Hak;Kim, Won Ho;Park, Hyung Soo
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.39 no.2
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    • pp.114-120
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    • 2019
  • This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation($R^2$) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The $R^2$ and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.