A simple seismic input model for estimating the seismic resistance of structures

The paper formulates the principles for shaping the design input, in particularly that the design input is not required to be similarto the real input. It is suggested that the seismic input should be set as a sinusoidal segment. This requires that the sinusoid be hazardous to the structure and causes it to reach the same limit state as a real earthquake. The amplitude of the sine wave is set equal to the average value of the peak boosts. The frequency of the exposure is set as dangerous for the structure to be designed and the duration is set according to the frequency of the exposure. The proposed seismic modelling approach makes it possible to assess the potential for progressive collapse and low-cycle fatigue of the designed structure. The model is based on statistical data on past earthquakes to estimate the average level of peak accelerations and the correlation between the prevailing period and the duration of the seismic event. The proposed input model greatly simplifies the computational assessment of seismic stability and the modeling of inputs on the seismic platform.

Detecting Bird and Frog Species Using Tropical Soundscape

In the tropical jungle, hearing a species is considerably simpler than seeing it. The sounds of many birds and frogs may be heard if we are in the woods, but the bird cannot be seen. It is difficult in this these circumstances for the expert in identifying the many types of insects and harmful species that may be found in the wild. An audio-input model has been developed in this study. Intelligent signal processing is used to extract patterns and characteristics from the audio signal, and the output is used to identify the species. Sound of the birds and frogs vary according to their species in the tropical environment. In this research we have developed a deep learning model, this model enhances the process of recognizing the bird and frog species based on the audio features. The model achieved a high level of accuracy in recognizing the birds and the frog species. The Resnet model which includes block of simple and convolution neural network is effective in recognizing the birds and frog species using the sound of the animal. Above 90 percent of accuracy is achieved for this classification task. Keywords: Bird Frog Detection, Neural Network, Resnet, CNN.

Speaker identification analysis for SGMM with k-means and fuzzy C-means clustering using SVM statistical technique

Speaker Identification denotes the speech samples of known speaker and it identifies the best matches of the input model. The SGMFC method is the combination of Sub Gaussian Mixture Model (SGMM) with the Mel-frequency Cepstral Coefficients (MFCC) for feature extraction. The SGMFC method minimizes the error rate, memory footprint and also computational throughput measure needs of a medium-vocabulary speaker identification system, supposed for preparation on a transportable or otherwise. Fuzzy C-means and k-means clustering are used in the SGMM method to attain the improved efficiency and their outcomes with parameters such as precision, sensitivity and specificity are compared.

Simulation of multi-power composite electric tractor based on power fluctuation ratio

All Combined with the characteristics of frequent abrupt load in the field operation of tractors, and aiming at the problems of single motor energy input and large fluctuation of battery state in the energy system of pure electric tractors, a power supply structure with multiple battery packs was proposed. A dynamic model considering real-time power and load fluctuation and a multi-power cooperative input model based on fuzzy control threshold logic rule based on power fluctuation ratio are established. Matlab and Simulink are used to simulate the model and compare it with the traditional single power model. The results show that when the speed is constant ploughing, the output power of the lithium battery of the multi-power cooperative input model is effectively compensated compared with that of the single-power model under sudden load. The average fluctuation ratio of rising power decreases from 5.8% / s to 2.7% / s, which realizes “peak clipping” and “slow peak” when the current fluctuates greatly. and then made the estimation of battery state of charge (SOC) more accurate, and prolonged the battery life.

Model Simulasi Nitrogen Pada Tanaman Tebu (Saccharum officinarum L.)

Tanaman tebu (Saccharum officinarum L.) merupakan komoditas yang mempunyai nilai ekonomis yang tinggi di Indonesia. Proses yang terjadi pada tanaman tebu dapat disederhanakan melalui model simulasi. Model simulasi digunakan untuk melihat pengaruh unsur cuaca dan pupuk terhadap perkembangan, pertumbuhan, dan poduktivitas tanaman. Dalam pemupukan yang perlu diperhatikan adalah efisiensi pemupukan. Kekurangan nitrogen akan menyebabkan tumbuhan tidak tumbuh secara optimum, sedangkan kelebihan nitrogen selain menghambat pertumbuhan tanaman juga akan menimbulkan pencemaran terhadap lingkungan. Jika nitrogen yang diserap dari tanah ke tanaman jumlahnya lebih kecil dari pada jumlah kebutuhan nitrogen tanamannya maka sebaiknya tanaman tersebut ditambahkan unsur hara nitrogen. Tahapan kegiatan penelitian terdiri atas 4 tahap yaitu penyusunan konsep model, penentuan nilai peubah dan parameter, input model, penyusunan model simulasi. Interaksi unsur cuaca yaitu curah hujan sangat menetukan kandungan air tanah yang yang berkaitan erat dengan proses mineralisasi (amonifikasi dan nitrifikasi) yang pada akhirnya akan mempengaruhi hasil produksi tanaman tebu. Curah hujan merupakan merupakan faktor yang mempengaruhi perubahan nitrogen dalam tanah. Model simulasi nitrogen ini dapat digunakan untuk menjelaskan proses perubahan nitrogen pada tanah dan tanaman tebu pada tingkat satu kali pemupukan standar. Pada tingkat pemupukan dua kali standar, model tidak dapat digunakan karena model tidak menjelaskan keseluruhan proses yang mempengaruhi.

Multiscale Bidirectional Input Convolutional and Deep Neural Network for Human Activity Recognition

In this paper, we proposed a multiscale and bidirectional input model based on convolutional neural network and deep neural network, named MBCDNN. In order to solve the problem of inconsistent activity segments, a multiscale input module is constructed to make up for the noise caused by filling. In order to solve the problem that single input is not enough to extract features from original data, we propose to manually design aggregation features combined with forward sequence and reverse sequence and use five cross-validation and stratified sampling to enhance the generalization ability of the model. According to the particularity of the task, we design an evaluation index combined with scene and action weight, which enriches the learning ability of the model to a great extent. In the 19 kinds of activity data based on scene+action, the accuracy and robustness are significantly improved, which is better than other mainstream traditional methods.

A Rapid Review of Image Captioning

Image captioning is an automatic process for generating text based on the content observed in an image. We do review, create framework, and build application model. We review image captioning into 4 categories based on input model, process model, output model, and lingual image caption. Input model is based on criteria caption, method, and dataset. Process model is based on type of learning, encoder-decoder, image extractor, and metric evaluation. Output model based on architecture, features extraction, feature aping, model, and number of caption. Lingual image caption based on language model with 2 groups: bilingual image caption and cross-language image caption. We also design framework with 3 framework model. Furthermore, we also build application with 3 application models. We also provide research opinions on trends and future research that can be developed with image caption generation. Image captioning can be further developed on computer vision versus human vision.

Prediction calculations for the first criticality of the HTR-PM using the PANGU code

The high-temperature reactor pebble-bed module (HTR-PM) is a modular high-temperature gas-cooled reactor demonstration power plant. Its first criticality experiment is scheduled for the latter half of 2021. Before performing the first criticality experiment, a prediction calculation was performed using PANGU code. This paper presents the calculation details for predicting the HTR-PM first criticality using PANGU, including the input model and parameters, numerical results, and uncertainty analysis. The accuracy of the PANGU code was demonstrated by comparing it with the high-fidelity Monte Carlo solution, using the same input configurations. It should be noted that keff can be significantly affected by uncertainties in nuclear data and certain input parameters, making the criticality calculation challenge. Finally, the PANGU is used to predict the critical loading height of the HTR-PM first criticality under design conditions, which will be evaluated in the upcoming experiment later this year.