Emotion Detection by Analyzing Voice Signal Using Wavelet

Emotion is such a unique power of human trial that plays a vital role in distinguishing human civilization from others. Voice is one of the most important media of expressing emotion. We can identify many types of emotions by talking or listening to voices. This is what we know as a voice signal. Just as the way people talk is different, so is the way they express emotions. By looking or hearing a person’s way of speaking, we can easily guess his/her personality and instantaneous emotions. People’s emotion and feelings are expressed in different ways. It is through the expression of emotions and feelings that people fully express his thoughts. Happiness, sadness, and anger are the main medium of expression way of different human emotions. To express these emotions, people use body postures, facial expressions and vocalizations. Though people use a variety of means to express emotions and feelings, the easiest and most complete way to express emotion and feelings is voice signal. The subject of our study is whether we can identify the right human emotion by examining the human voice signal. By analyzing the voice signal through wavelet, we have tried to show whether the mean frequency, maximum frequency and Lp values conform to a pattern according to its different sensory types. Moreover, the technique applied here is to develop a concept using MATLAB programming, which will compare the mean frequency, maximum frequency and Lp norm to find relation and detect emotion by analyzing different voices.

A new bracing system for improvement of seismic performance of steel jacket type offshore platforms with float-over-deck

In this paper, the seismic response of a newly designed steel jacket offshore platform with a float over deck （FOD） system in the Persian Gulf was investigated through incremental dynamic analysis. Comparison of incremental dynamic analysis results for both directions of the platform shows that the lateral strength of the platform in the float over direction is less than its lateral strength in other direction. Dynamic characteristics measurement of a scale model of platform was also performed using forced vibration tests. From experimental measurement of the scaled model, it was observed that dynamic characteristic of the platform is different in the float over direction compared to the other direction. Therefore, a new offshore installed bracing system for the float over direction was proposed for improvement of seismic performance of this type of platform. Finally, the structure with the modified system was assessed using the probabilistic seismic assessment method as well as experimental measurement of its dynamic characteristics. It was observed that the proposed offshore installed bracing system improves the performance of platforms subjected to strong ground motion.

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of promi-nently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing(MIMO–OFDM).One among an effective solution for channel estimation in wireless communication system,spe-cifically in different environments is Deep Learning(DL)method.This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder(CNNAE)classifier for MIMO-OFDM systems.A CNNAE classi-fier is one among Deep Learning(DL)algorithm,in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another.Improved performances are achieved by using CNNAE based channel estimation,in which extension is done for channel selection as well as achieve enhanced performances numerically,when compared with conventional estimators in quite a lot of scenar-ios.Considering reduction in number of parameters involved and re-usability of weights,CNNAE based channel estimation is quite suitable and properlyfits to the video signal.CNNAE classifier weights updation are done with minimized Sig-nal to Noise Ratio(SNR),Bit Error Rate(BER)and Mean Square Error(MSE).

Study of Denoising in the Electricity Anti-Stealing Detection Based on VMD-WTD Combination

In order to solve the failure of electricity anti-stealing detection device triggered by the noise mixed in high-frequency electricity stealing signals,a denoising method based on variational mode decomposition(VMD)and wavelet threshold denoising(WTD)was applied to extract the effective high-frequency electricity stealing signals.First,the signal polluted by noise was pre-decomposed using the VMD algorithm,the instantaneous frequency means of each pre-decomposed components was analyzed,so as to determine the optimal K value.The optimal K value was used to decompose the polluted signal into K intrinsic mode components,and the sensitive mode components were determined through the cross-correlation function.Next,each sensitive mode was reconstructed.Finally,the reconstructed signal denoised using the wavelet threshold to obtain the denoised signal.The simulation analysis and experimental results show that the proposed method is superior to the traditional VMD method,FFT method and EMD method,as it can effectively eliminate the noise and enhance the reliability of high-frequency electricity stealing signal detection.

Probabilistic safety assessment of self-centering steel braced frame

The main drawback of conventional braced frames is implicitly accepting structural damage under the design earthquake load, which leads to considerable economic losses. Controlled rocking self-centering system as a modem low-damage system is capable of minimizing the drawbacks of conventional braced frames. This paper quantifies main limit states and investigates the seismic performance of self-centering braced frame using a Probabilistic Safety Assessment procedure. Margin of safety, confidence level, and mean annual frequency of the self-centering archetypes for their main limit states, including PT yield, fuse fracture, and global collapse, are established and are compared with their acceptance criteria. Considering incorporating aleatory examined. Results of the investigation indicate that the provide the adequate margin of safety against exceeding and epistemic uncertainties, the efficiency of the system is design of low- and mid-rise self-centering archetypes could the undesirable limit-states.