Design Of Simple Model Research Articles

Temporal feature decomposition fusion network for building energy multi-step prediction

Accurate building energy prediction methods have become a key factor in achieving energy-saving goals. Traditional methods for building energy multi-step prediction often use recursive or direct strategies to address time series prediction problems, which may neglect the data sequence correlation and result in the cumulative error. To solve the above problem, this paper proposed a Temporal Feature Decomposition Fusion Network (TFDFNet) model for building energy consumption multi-step prediction, with an encoder-decoder architecture. In the encoder, feature fusion layers is employed to consider the influence of different feature sequences on the predicted sequence. Through the decomposition of historical load data sequences, different hierarchical sequence structures are constructed to enhance the interpretability and predictability of the data. In the decoder, a simple and efficient network is constructed using MLP to decode the encoded information and obtain the prediction results. Experimental results show that, the proposed model achieves higher prediction accuracy and more stable convergence than the other five comparable methods, which also indicates the potential of achieving excellent building energy consumption multi-step prediction results with a simple model design.

An improved generative network model for tackling mode collapse in medical leaf image generation

In this study, a unique generative adversarial network (GAN) architectural variation was suggested, which engages in adversarial game serve by preserving an appropriate distance in the latent dimension of the network. This method overcomes the mode collapse problem with a small dataset. Extensive experiments are conducted using the segmented medical leaf dataset with various classes and the generator network is able to produce all the artificial image classes. This is accomplished by combining a unique training technique with a reasonably simple model design.

Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation.

Accurate identification of high-frequency oscillation (HFO) is an important prerequisite for precise localization of epileptic foci and good prognosis of drug-refractory epilepsy. Exploring a high-performance automatic detection method for HFOs can effectively help clinicians reduce the error rate and reduce manpower. Due to the limited analysis perspective and simple model design, it is difficult to meet the requirements of clinical application by the existing methods. Therefore, an end-to-end bi-branch fusion model is proposed to automatically detect HFOs. With the filtered band-pass signal (signal branch) and time-frequency image (TFpic branch) as the input of the model, two backbone networks for deep feature extraction are established, respectively. Specifically, a hybrid model based on ResNet1d and long short-term memory (LSTM) is designed for signal branch, which can focus on both the features in time and space dimension, while a ResNet2d with a Convolutional Block Attention Module (CBAM) is constructed for TFpic branch, by which more attention is paid to useful information of TF images. Then the outputs of two branches are fused to realize end-to-end automatic identification of HFOs. Our method is verified on 5 patients with intractable epilepsy. In intravalidation, the proposed method obtained high sensitivity of 94.62%, specificity of 92.7%, and F1-score of 93.33%, and in cross-validation, our method achieved high sensitivity of 92.00%, specificity of 88.26%, and F1-score of 89.11% on average. The results show that the proposed method outperforms the existing detection paradigms of either single signal or single time-frequency diagram strategy. In addition, the average kappa coefficient of visual analysis and automatic detection results is 0.795. The method shows strong generalization ability and high degree of consistency with the gold standard meanwhile. Therefore, it has great potential to be a clinical assistant tool.

Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality.

Sale and Leaseback Revisited

The FASB’s Accounting Standards Update (ASU) is intended to improve financial reporting about leasing transactions including the sale and leaseback arrangements and will take effect as of December 15, 2018, for public companies. In this article, we present a methodology for a structured approach to sale and leaseback arrangements. First, we revisit and identify an array of issues and critical variables. Then, we utilize a spreadsheet model to illustrate the sale and leaseback arrangement with an example. Our model allows the lease to specify the terms of transactions, including lease payments, length and type of the lease, and other conditions. Finally, the model calculates the net present value of the transaction. The robust but simple model design can be customized to conduct any sensitivity analysis.

Engineering and design of simple models from dye-sensitive of solar cells and photovoltaic cells applications: Theoretical study

In this study, the optimization geometric and optoelectronic properties of new structures that based on quater[1,3,4]thiadiazole and benzothiophene were calculated by using density functional theory (DFT) and time-dependent (TDDFT) at B3LYP and the 6-31G(d) basis set. The total energy, orbital (HOMO, LUMO) distributions, the energy gaps, the maximum open circuit voltage, maximum wavelength absorption, vertical absorption energies, and oscillator strengths have been investigated and discussed. We studied and discussed effect the donor group substituents (COH, and CP) and the acceptor group substituents (Br, OH, Cl, F, and CN) on optimization geometric, electronic properties, and optical properties the molecules, we observed that the donor group substituents lead to decrease energy gap and the acceptor group substituents lead to increase energy gap. These changes give alteration of maximum wavelengths absorption from 453.1 to 559.9 nm. Therefore, these compounds have major absorption bands inside the solar spectrum, to give a present better performance for the solar cells and photovoltaic devices.

Influence of the Helical Backbone in the Behavior of a Simple Model for Dimeric Coiled-Coil Proteins

Using computer simulations as a tool for thought experiments, we investigate the influence of the helical backbone geometry in the association process and the final structures of simple model which mimics parallel, two-stranded coiled-coil proteins. We define three types of helices: two of them have straight helical axes and 3.5 or 3.6 residues per helical turn; the third type presents a coiled helical axis, according to the canonical scheme defined by Crick. By using a Monte Carlo simulation algorithm, we observe that the three models exhibit different transition temperatures for the formation of the dimeric structure from two independent peptides, and a different behavior concerning the appearance of out-of-register structures. The energy minimized dimer structures present strong deviations from the correct association for straight helices with 3.6 residues/ turn, especially for long peptides, deviations which are absent for the other two types when only the burial of hydrophobic residues is considered. A careful analysis of the energies for the out-of-register configurations and the contact maps reveals also differences between dimers resulting from the model with Crick parameterization and with 3.5 residues/ turn, The results presented in this paper may be relevant for the design of simple models which use rigid α-helices built from predicted elements of secondary structure.