scholarly journals Application of Temperature Modulation-SDP on MOS Gas Sensors: Capturing Soil Gaseous Profile for Discrimination of Soil under Different Nutrient Addition

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Arief Sudarmaji ◽  
Akio Kitagawa
Keyword(s):  
Gas Sensors ◽  
Sandy Loam ◽  
Complex Mixture ◽  
Principal Component ◽  
Classification Performance ◽  
Temperature Modulation ◽  
Sigmoid Function ◽  
Nutrient Additions ◽  
Detection Point ◽  
Gaseous Compound

A technique of temperature modulation-SDP (specified detection point) on MOS gas sensors was designed and tested on their sensing performance to such complex mixture, soil gaseous compound. And a self-made e-nose was built to capture and analyze the gaseous profile from sampling headspace of two soils (sandy loam and sand) with the addition of nutrient at different dose (without, normal, and high addition). It comprises (a) 6 MOS gas sensors which were driven wirelessly on a certain modulation through (b) a PSoC CY8C28445-24PVXI-based interface and (c) the Principal Component Analysis (PCA) and neural network (NN) as pattern recognition tools. The gaseous compounds are accumulated in a static headspace with thermostatting and stirring under controlled condition to optimize equilibration and gases concentration as well. The patterns are trained by backpropagation algorithm which employs a log-sigmoid function and updates the weights using search-then-converge schedule. PCA results indicate that the sensor array used is able to differentiate the soil type clearly and may provide a discrimination as a response to presence/level of the nutrients addition in soil. Additionally, the PCA enhances the classification performance of NN to discriminate among the predescribed nutrient additions.

scholarly journals Measurement of Patchouli oil vapor using array of MOS gas sensors in various adulteration substances and concentrations

Food Research ◽  
2021 ◽  
Vol 5 (S2) ◽  
pp. 84-90
Author(s):  
A. Sudarmaji ◽  
A. Margiwiyatno ◽  
S.B. Sulistyo ◽  
Saparso
Keyword(s):  
Palm Oil ◽  
Gas Sensors ◽  
World Market ◽  
Classification Performance ◽  
Oxide Semiconductor ◽  
Individual Response ◽  
Temperature Modulation ◽  
High Demand ◽  
Pca Method ◽  
Semiconductor Gas Sensors

Indonesia is one of the main suppliers of Patchouli oil in the world market. It has high economical value. Indonesian Patchouli oil is mostly produced by SMEs using the distillation technique. However, the high demand and price of Patchouli oil led to the fraud of adulteration. SMEs intentionally mixed Patchouli oil with cheaper oils. This paper presented the vapor measurement of Patchouli oil by using an array of metal oxide semiconductor gas sensors (MOS) which may apply to indicate the presence of adulteration substance in Patchouli oil. A total of nine MOS gas sensors were tested. All MOS are driven with temperature modulation technique. We built an acquisition unit based on the PSoC device to acquire the MOS outputs to a computer. We tested two adulteration substances (palm oil and biodiesel oil), and two compositions (1:3 and 1:5) on two levels of Patchouli oil. Individual response of MOS was examined. The Principle Component Analysis (PCA) method was used to show the classification performance to distinguish the adulteration types in Patchouli oil. We found that there was no single MOS that able to distinguish the adulteration individually, and there were many overlapping responses to adulteration substances and compositions. The PCA results showed that on each level of Patchouli oil, nine MOS gas sensors can distinguish clearly between the with and without adulteration substances (palm oil and biodiesel oil).

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1407
Author(s):  
Matyas Bukva ◽  
Gabriella Dobra ◽  
Juan Gomez-Perez ◽  
Krisztian Koos ◽  
Maria Harmati ◽  
...  
Keyword(s):  
Lumbar Disc ◽  
Area Under The Curve ◽  
Principal Component ◽  
Extracellular Vesicle ◽  
Classification Performance ◽  
Cns Tumors ◽  
Clinical Samples ◽  
Support Vector ◽  
Serum Samples ◽  
Raman Spectroscopic

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis–Support Vector Machine (PCA–SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9–92.5% CA, 80–95% sensitivity and 80–90% specificity. AUC scores in the range of 0.82–0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil

2019 ◽  
Vol 73 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Yun Zhao ◽  
Mahamed Lamine Guindo ◽  
Xing Xu ◽  
Miao Sun ◽  
Jiyu Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
Principal Component ◽  
Classification Performance ◽  
Soil Samples ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Different Levels

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.

Variability in Muscle Recruitment Strategy Between Operators During Assisted Assembly Tasks

2018 ◽  
Author(s):  
Yingxin Qiu ◽  
Keerthana Murali ◽  
Jun Ueda ◽  
Atsushi Okabe ◽  
Dalong Gao
Keyword(s):  
Upper Limb ◽  
Lower Limb ◽  
Wrist Joint ◽  
Principal Component ◽  
Classification Performance ◽  
Body Motion ◽  
Recruitment Strategy ◽  
Support Vector ◽  
Recruitment Strategies ◽  
Muscle Recruitment

This paper reports the variability in muscle recruitment strategies among individuals who operate a non-powered lifting device for general assembly (GA) tasks. Support vector machine (SVM) was applied to the classification of motion states of operators using electromyography (EMG) signals collected from a total of 15 upper limb, lower limb, shoulder, and torso muscles. By comparing the classification performance and muscle activity features, variability in muscle recruitment strategy was observed from lower limb and torso muscles, while the recruitment strategies of upper limb and shoulder muscles were relatively consistent across subjects. Principal component analysis (PCA) was applied to identify key muscles that are highly correlated with body movements. Selected muscles at the wrist joint, ankle joint and scapula are considered to have greater significance in characterizing the muscle recruitment strategies than other investigated muscles. PCA loading factors also indicate the existence of body motion redundancy during typical pick-and-place tasks.

scholarly journals Feature Selection using Genetic Programming

2019 ◽  
Vol 3 (2) ◽  
pp. 11-18
Author(s):  
George Mweshi
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Information Gain ◽  
Principal Component ◽  
Search Space ◽  
Classification Performance ◽  
The Other ◽  
Searching Strategy ◽  
Mining Algorithms ◽  
Feature Selection Techniques

Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.

Design and Development of EMG Conditioning System and Hand Gesture Recognition Based on Principal Component Analysis Feature Reduction Technique

2014 ◽  
pp. 304-320
Author(s):  
P. Geethanjali
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Digital Signal ◽  
Component Analysis ◽  
Classification Performance ◽  
Feature Reduction ◽  
Design And Development ◽  
Emg Signal ◽  
Knn Classifier ◽  
Power Line Interference

This chapter discusses design and development of a surface Electromyogram (EMG) signal detection and conditioning system along with the issues of gratuitous spurious signals such as power line interference, artifacts, etc., which make signals plausible. In order to construe the recognition of hand gestures from EMG signals, Time Domain (TD) and well as Autoregressive (AR) coefficients features are extracted. The extracted features are diminished using the Principal Component Analysis (PCA) to alleviate the burden of the classifier. A four-channel continuous EMG signal conditioning system is developed and EMG signals are acquired from 10 able-bodied subjects to classify the 6 unique movements of hand and wrist. The reduced statistical TD and AR features are used to classify the signal patterns through k Nearest Neighbour (kNN) as well as Neural Network (NN) classifier. Further, EMG signals acquired from a transradial amputee using 8-channel systems for the 6 amenable motions are also classified. Statistical Analysis of Variance (ANOVA) results on classification performance of able-bodied subject divulge that the performance TD-PCA features are more significant than the AR-PCA features. Further, no significant difference in the performance of NN classifier and kNN classifier is construed with TD reduced features. Since the average classification error of kNN classifier with TD features is found to be less, kNN classifier is implemented in off-line using the TMS2407eZdsp digital signal controller to study the actuation of three low-power DC drives in the identification of intended motion with an able-bodied subject.

A Strategy to Improve O2 Gas Response of La-SnO2 Nanofibers Based Sensor through Temperature Modulation

2019 ◽  
Vol 944 ◽  
pp. 657-665
Author(s):  
Ya Xiong ◽  
Hui Li ◽  
Tian Chao Guo ◽  
Qing Zhong Xue
Keyword(s):  
Gas Sensors ◽  
Gas Sensing ◽  
Operation Mode ◽  
Temperature Modulation ◽  
Oxide Semiconductors ◽  
Oxygen Ions ◽  
Isothermal Test ◽  
Sensor Temperature ◽  
Heating Energy Consumption ◽  
Gas Response

Generally sensing mechanisms of gas sensors based on metal-oxide semiconductors greatly depend on temperature, suggesting temperature modulation can be applied as a vital method to effectively enhance the sensor response. In this paper, we reported a strategy of quick-cooling operating temperature mode in the course of gas sensing process to elevate the O2 gas response while maintaining low heating energy consumption. La-SnO2 nanofibers synthesized by electrospinning were chosen as gas sensing materials. The O2 gas responses by employing quick-cooling operation mode are significantly improved compared with those obtained by traditional isothermal test. The improved O2 response is contributed to a higher coverage of negatively charged oxygen ions as a result of quick cooling. Our research offers a facile route to detect gas at low temperature with high response. More importantly, the strategy demonstrated here could also be extended to other gas sensor as long as its gas response is related to the sensor temperature.

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