Psycholinguistic Preconditions of Speech Formation of Children with General Speech Retardation

The article addresses psycholinguistic preconditions for development of the communicative component of speech activity in 5-year-olds with general speech retardation (GSR). The development of speech activity is analyzed through the lens of psycholinguistic motivation for the emergence of speech units. The authors for the first time identified psychological mechanisms that underlie disorders in the development of the communication component of speech activity in 5-year-olds with GSR and suggested effective interventions. The research involved a study of probability prediction within the structure of the communicative component of speech activity of 5-year-olds with GSR. The author-developed classification of non-verbal and verbal probability prediction formed the basis for a theory-based diagnostic tool to assess the communicative component of speech activity in 5-year-olds with GSR. The research demonstrated the importance of probability prediction as a dynamic process and indicator of practical realization of utterance and holistically developed coherent speech. The analysis of disorders in cognitive and speech operations and functions identified in the study points to the dominant role of weak probability prediction function at non-verbal and verbal levels. Weak probability prediction was defined as the cause of poorly developed communication component of speech activity in 5-year-olds with GSR.

DAYLIGHT GLARE PROBABILITY PREDICTION FOR AN OFFICE ROOM

Glare assessments are currently made from High Dynamic Range (HDR) images taken from the Point Of View (POV) and viewing direction of a user. This paper analyses the feasibility to estimate the Daylight Glare Probability (DGP) at the user-level based on machine-learning techniques, sun position and a downward-pointing camera sensor mounted at the ceiling of a simulated office environment. Three different office cases have been considered: an empty room, an empty room with venetian blinds and a furnished room without venetian blinds. The influence of the sun direction has been considered as a parameter to predict the observer DGP. Subsequently, the best parameters have been selected to build a black box model using Artificial Intelligence (AI). Results show that, by using the DGP of the ceiling camera and the sun position, it is possible to accurately predict the DGP for an observer’s POV.

Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.

Construction of Hybrid Model for English News Headline Sarcasm Detection by Word Embedding Technique

People often use sarcasm to taunt, anger, or amuse one another. Scathing undertones can't be missed, even when using a simple sentiment analysis tool. Sarcasm may be detected using a variety of machine learning techniques, including rule-based approaches, statistical approaches, and classifiers. Since English is a widely used language on the internet, most of these terms were created to help people recognize sarcasm in written material. Convolutional Neural Networks (CNNs) are used to extract features, and Naive Bayes (NBs) are trained and evaluated on those features using a probability function. This suggested approach gives a more accurate forecast of sarcasm detection based on probability prediction. This hybrid machine learning technique is evaluated according to the stretching component in frequency inverse domain, the cluster of the words and word vectors with embedding. Based on the findings, the proposed model surpasses many advanced algorithms for sarcasm detection, including accuracy, recall, and F1 scores. It is possible to identify sarcasm in a multi-domain dataset using the suggested model, which is accurate and resilient.

Fundamental study on the thermodynamic non-equilibrium freezing probability prediction model taking into account the super cooling phenomena in porous material

It is commonly considered that frost damage is caused by sudden freezing of supercooled water, which is a random phenomenon. Therefore, the aims of this study are to establish a prediction model for the probability of freezing until any lowest reached temperature, and to obtain the probability distribution function of the freezing point for the proposed analytical prediction model. First, theoretical prediction model for the probability of the instantaneous increment of ice content when lowest achieving temperature was known was derived based on these assumptions that building structure is an aggregation of small elements. Next, the freezing point measurement was carried out by using saturated mortar samples as the small element. As the results, it could be found that the first freezing due to supercooling occurred from -4 to -11 deg. C and the maximum probability was appeared at -7.5 deg. C. The average increment of ice content at every temperature closed to the 40 % volume of pore water until the thermodynamically-based freezing point. Moreover, the proposed method that can calculate the probability distributions of the instantaneous increment of ice content for any lowest achieving temperature from pore size distribution had good agreements with the measurement results.

Development of Heatwave Probability Prediction Model Based on Recurrent Neural Network: A Case of Seoul Metropolitan Government

Recently, the incidence of heat waves has increased due to climate change, and the resultant mortalities and socio-economic damage are also increasing in Korea. Hence, emphasis has been placed on research examining heatwaves and their effects. Predicting the probability of heatwave in advance is very important from the perspective of disaster risk management; however, related studies have been insufficient so far. Therefore, in this study, the probability of future heatwave onset was predicted using daily scaled past weather data for Seoul Metropolitan Government. For the analysis, models based on recurrent neural networks (RNN, LSTM, GRU) were used, which are suitable for analyzing time-series data. Upon evaluating the performance of the GRU model, which was selected as the optimized model, no overfitting problem was observed. The prediction accuracy of the model was high as it demonstrated a reproduction of 78% and 86% of actual heatwave days during the validation and test process, respectively. Therefore, this model can be used by each local government to coordinate an efficient response to heat waves.

ProbFire: a probabilistic fire early warning system for Indonesia

Recurrent extreme landscape fire episodes associated with drought events in Indonesia pose severe environmental, societal and economic threats. The ability to predict severe fire episodes months in advance would enable relevant agencies and communities more effectively initiate fire preventative measures and mitigate fire impacts. While dynamic seasonal climate predictions are increasingly skilful at predicting fire-favourable conditions months in advance in Indonesia, there is little evidence that such information is widely used yet by decision makers. In this study, we move beyond forecasting fire risk based on drought predictions at seasonal timescales, and (i) develop a probabilistic early fire warning system for Indonesia (ProbFire) based on multilayer perceptron model using ECMWF SEAS5 dynamic climate forecasts together with forest cover, peatland extent and active fire datasets that can be operated on a standard computer, (ii) benchmark the performance of this new system for the 2002–2019 period, and (iii) evaluate the potential economic benefit such integrated forecasts for Indonesia. ProbFire's event probability predictions outperformed climatology-only based fire predictions at three to five-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at one month lead time, while in west Kalimantan seasonal predictions did not offer any additional benefit over climatology only-based predictions. We (i) find that seasonal climate forecasts coupled with the fire probability prediction model confer substantial benefits to a wide range of stakeholders involved in fire management in Indonesia and (ii) provide a blueprint for future operational fire warning systems that integrate climate predictions with non-climate features.