Temporal-Sound based User Interface for Smart Home

2021 ◽  
Author(s):  
Kido Tani ◽  
Nobuyuki Umezu
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Smart Home ◽  
Classification Learning ◽  
Activity Classification ◽  
Smart Watch ◽  
Acceleration Data ◽  
Physical Controls ◽  
The Difference

We propose a gesture-based interface to control a smart home. Our system replaces existing physical controls with our temporal sound commands using accelerometer. In our preliminary experiments, we recorded the sounds generated by six different gestures (knocking the desk, mouse clicking, and clapping) and converted them into spectrogram images. Classification learning was performed on these images using a CNN. Due to the difference between the microphones used, the classification results are not successful for most of the data. We then recorded acceleration values, instead of sounds, using a smart watch. 5 types of motions were performed in our experiments to execute activity classification on these acceleration data using a machine learning library named Core ML provided by Apple Inc.. These results still have much room to be improved.

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

scholarly journals SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model

2021 ◽  
Vol 11 (6) ◽  
pp. 2784
Author(s):  
Shahnaz TayebiHaghighi ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Rolling Element Bearing ◽  
Crack Identification ◽  
Original Signal ◽  
Signal Modeling ◽  
Residual Signal ◽  
Average Accuracy ◽  
Self Tuning ◽  
The Difference

In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.

scholarly journals Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jung Eun Huh ◽  
Seunghee Han ◽  
Taeseon Yoon
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
High Similarity ◽  
Incubation Periods ◽  
Initial Question ◽  
The Difference ◽  
Blast Program

Abstract Objective In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms. Results The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

2021 ◽  
Author(s):  
Yashas Samaga B L ◽  
Shampa Raghunathan ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Protein Stability ◽  
Data Augmentation ◽  
Stability Prediction ◽  
Structural Environment ◽  
Self Consistent ◽  
The Difference ◽  
Protein Sequence Space ◽  
New Machine

<div>Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency, and a new machine learning based method, first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that estimates a residue's contributions towards protein stability dG in its local structural environment. The difference between independently predicted contributions of the reference and mutant residues in a missense mutation is reported as dG. We show that this self-consistent machine learning architecture is immune to many common biases in datasets, relies less on data than existing methods, and is robust to overfitting.</div><div><br></div>

scholarly journals Physical controls on the storage of methane in landfast sea ice

2014 ◽  
Vol 8 (3) ◽  
pp. 1019-1029 ◽  
Author(s):  
J. Zhou ◽  
J.-L. Tison ◽  
G. Carnat ◽  
N.-X. Geilfus ◽  
B. Delille
Keyword(s):  
Sea Ice ◽  
Bubble Formation ◽  
Gas Bubble ◽  
Physical Processes ◽  
Ice Melt ◽  
Physical Controls ◽  
The Difference ◽  
Landfast Sea Ice ◽  
Ice Water ◽  
Brine Concentration

Abstract. We report on methane (CH4) dynamics in landfast sea ice, brine and under-ice seawater at Barrow in 2009. The CH4 concentrations in under-ice water ranged from 25.9 to 116.4 nmol L−1sw, indicating a supersaturation of 700 to 3100% relative to the atmosphere. In comparison, the CH4 concentrations in sea ice ranged from 3.4 to 17.2 nmol L−1ice and the deduced CH4 concentrations in brine from 13.2 to 677.7 nmol L−1brine. We investigated the processes underlying the difference in CH4 concentrations between sea ice, brine and under-ice water and suggest that biological controls on the storage of CH4 in ice were minor in comparison to the physical controls. Two physical processes regulated the storage of CH4 in our landfast ice samples: bubble formation within the ice and sea ice permeability. Gas bubble formation due to brine concentration and solubility decrease favoured the accumulation of CH4 in the ice at the beginning of ice growth. CH4 retention in sea ice was then twice as efficient as that of salt; this also explains the overall higher CH4 concentrations in brine than in the under-ice water. As sea ice thickened, gas bubble formation became less efficient, CH4 was then mainly trapped in the dissolved state. The increase of sea ice permeability during ice melt marked the end of CH4 storage.

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