Robust cross-subject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of latent that related to processing must contribute building robust models. Specifically, propose utilize unsupervised deep generative model (e.g., variational autoencoder), determine factors from EEG. Through sequence modeling method, examine performance learnt factors. The...

Drug target interactions (DTIs) play a crucial role in drug discovery and development. The prediction of DTIs based on computational method can effectively assist the experimental techniques for identification, which are time-consuming expensive. However, current models suffer from low accuracy high false positive rate DTIs, especially datasets with extremely unbalanced sample categories. To accurately identify interaction between drugs proteins, variety descriptors that fully show...

This study proposes a new resonant magnetic charger comprising circular spiral coils that operate with strong coupling effect between the transmitter and receiver. The two are fitted additional copper tapes to serve as receiver coils. flux distributions calculated using temporal coupled mode theory. Analysis results show proposed system can dramatically improve efficiency extend power transfer distance. Experiments have been carried out in order verify performance of system. In particular,...

This paper proposes a dynamic programming (DP)-based stochastic model predictive control (SMPC) method for the economic operation of solar PV-powered ice-storage air-conditioning (PIAC) systems. The forecast data PV generation and building cooling load are considered as variables in this paper. To deal with uncertainties day-ahead data, Latin hypercube sequential sampling, Cholesky decomposition Simultaneous backward reduction adopted to provide representative scenarios SMPC. value function...

Introduction Currently, deep-learning-based prediction of Significant Wave Height (SWH) is mostly performed for a single location in the ocean or simply relies on factor (SF). Such approaches have disadvantage lacking spatial correlations dynamic complexity, leading to an inevitable growth error with time. Methods Here, attempting solution, we develop Multi-Factor (MF) data-driven 2D SWH model Bohai, Yellow, and East China Seas (BYECS). Our developed based multi-channel PredRNN algorithm...

Right now, the diagnosis of coronary heart disease is mostly from experienced physician's judgment. How to use computer intelligent algorithms aid in diagnosing has been a hot research machine learning. This article will apply support vector (SVM) method which based on statistical learning theory disease. On basis original data pre-processing and feature extraction, classifiers with different kernel are selected classify test data, followed by comparison classification results show that...

Robust cross-subject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of latent that related to processing must contribute building robust models. We propose utilize variational autoencoder (VAE) determine factors from EEG. Through sequence modeling method, examine performance learnt factors. The proposed methodology is verified two public...

Real-time acquisition of coal stress is the key to early warning and prevention rock burst. In drilling process, parameters will change correspondingly with stress. This paper, according bit link, establishes a theoretical model in order verify reliability specific work crushing, use self-developed measurement experimental device, develop tests on raw samples under different lateral stresses. Meanwhile, we study variation law reveal energy conversion mechanism machine chip surface energy....

The underlying complexity of urban space can be manifested by its fractal forms and scaling statistics. This paper examines these characteristics at the intra-urban scale through lens clustered street junctions (including road ends) in two Chinese metropolitan areas: Beijing Shenzhen. We derived cluster sets with Euclidean distance thresholds starting 100 meters (m) ending 1000 m, outlined each using a concave-hull method to maintain their original irregular shapes. Within delimited cluster,...

A pattern-set generation algorithm (PSG) for the one-dimensional multiple stock sizes cutting problem (1DMSSCSP) is presented. The solution process contains two stages. In first stage, PSG solves residual problems repeatedly to generate patterns in pattern set, where each solved by column-generation approach, and generated solving a single large object placement problem. second integer linear programming model of 1DMSSCSP using commercial solver, only set are considered. computational...

With the rise in use of DC distributed energy resources and growth electricity load, difficulty improving power quality has become an important research direction. The on impact development distribution theory technology. In this paper, evaluation method that combines empirical mode decomposition (EMD) with a one-dimensional convolutional neural network (1D-CNN) is proposed. As data preprocessing, EMD decomposes original electrical signal into several intrinsic functions (IMFs). Then, 1-D...

Georeferencing by place names (known as toponyms) is the most common way of associating textual information with geographic locations. While computers use numeric coordinates (such longitude-latitude pairs) to represent places, people generally refer places via their toponyms. Query toponym an effective find about a area. However, segmenting and parsing addresses extract local toponyms difficult task in geocoding field, especially China. In this paper, spatial context-based framework...

Considering the correlations of input indexes and deficiency calibrating kernel function parameters when support vector machine (SVM) is applied, a forecasting method based on principal component analysis-genetic algorithm-support (PCA-GA-SVM) proposed to improve precision bus arrival time prediction.And No. 232 in Shenyang City China taken as an example.The traditional SVM Kalman Filtering model GA-SVM are also employed make comparative analysis prediction rate, respectively.The result...

This paper presents a new dual-core closed-loop flux gate current sensor. The mathematical model was built, and the parameters that affect characteristics of sensor were analyzed. effects structural on output simulated. Based simulation results, prototype designed, test results have good agreement with results. has advantages ultra low nonlinear error ( ± 3‰) offset error. noise in primary is eliminated when measuring dc or ac signal. Especially, designed can measure up to 20 kHz nominal value 30 A.