scholarly journals Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1817 ◽  
Author(s):  
Zhiyuan Wu ◽  
Hang Wang ◽  
Xiping Wang ◽  
Hunlong Zheng ◽  
Zhiming Chen ◽  
...  
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Test Results ◽  
Mosquito Repellent ◽  
Accuracy Rate ◽  
Qualitative And Quantitative ◽  
Flammable Liquids ◽  
Quantitative Monitoring ◽  
Electric Nose ◽  
Metal Oxide Sensor

A real-time electric nose (E-nose) with a metal oxide sensor (MOS) array was developed to monitor 5 highly flammable liquids (ethanol, tetrahydrofuran, turpentine, lacquer thinner, and gasoline) in this work. We found that temperature had a significant impact on the test results and temperature control could efficiently improve the performance of our E-nose. The results of our qualitative analysis showed that principal component analysis (PCA) could not efficiently distinguish these samples compared to a back-propagation artificial neural network (BP-ANN) which had a 100% accuracy rate on the test samples. Quantitative analysis was performed by regression analysis and the average errors were 9.1%–18.4%. In addition, through anti-interference training, the E-nose could filter out the potential false alarm caused by mosquito repellent, perfume and hair jelly.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

scholarly journals The Efficiency of Microstrainers Filtration in the Process of Removing Phytoplankton with Special Consideration of Cyanobacteria

Toxins ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 285 ◽  
Author(s):  
Wanda Czyżewska ◽  
Marlena Piontek
Keyword(s):  
Water Treatment ◽  
Surface Water ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Filtration Process ◽  
Particle Filtration ◽  
Qualitative And Quantitative ◽  
Water Treatment Process ◽  
Adverse Impacts ◽  
Quantitative Monitoring

The research presented in this manuscript concerns the evaluation of the effectiveness of microstrainers, which are designed to reduce the amount of plankton in treated surface water. The efficiency of microstrainer filtration analysis is very important for the proper course of the water-treatment process not only in the Water-Treatment Plant (WTP) in Zielona Góra (central western Poland) but also in other WTPs around the world. The qualitative and quantitative monitoring of the abundance of plankton including cyanobacteria during the particle-filtration process allows not only for the assessment of the potential cyanotoxic risk in surface water providing a source of drinking water, but also allows the evaluation of the action and the prevention of adverse impacts of microstrainers. Over four years of research, it was observed that the largest amount of cyanobacteria before microstrainer filtration took place in May. The dominant species was Limnothrix redeckei. The microstrainer removal of plankton and cyanobacteria was statistically significant. The quantity of removed plankton increased with its increasing content in raw water. The particle-filtration process, by reducing the amount of cyanobacteria, contributes to a decrease in intracellular microcystins.

Improved AEB algorithm combined with estimating the adhesion coefficient of road ahead and considering the performance of EHB

2021 ◽  
pp. 095440702110261
Author(s):  
Dequan Zeng ◽  
Zhuoping Yu ◽  
Lu Xiong ◽  
Junqiao Zhao ◽  
Peizhi Zhang ◽  
...  
Keyword(s):  
Joint Probability ◽  
Principal Component ◽  
Test Results ◽  
Safe Distance ◽  
Adhesion Coefficient ◽  
The Road ◽  
Response Characteristics ◽  
Emergency Braking ◽  
Multiple Beams ◽  
Maximum Method

This paper proposes an improved autonomous emergency braking (AEB) algorithm intended for intelligent vehicle. Featuring a combination with the estimation of road adhesion coefficient, the proposed approach takes into account the performance of electronic hydraulic brake. In order for the accurate yet fast estimate of road ahead adhesion coefficient, the expectation maximization framework is applied depending on the reflectivity of ground extracted by multiple beams lidar in four major steps, which are the rough extraction of ground points based on 3 σ criterion, the accurate extraction of ground points through principal component analysis (PCA), the main distribution characteristics of ground as extracted using the expectation maximum method (EM) and the estimation of road adhesion coefficient via joint probability. In order to describe the performance of EHB, the response characteristics, as well as the forward and adverse models of both braking pressure and acceleration are obtained. Then, with two typical roads including single homogeneous road and fragment pavement, the safe distance of improved AEB is modeled. To validate the algorithm developed in this paper, various tests have been conducted. According to the test results, the reflectivity of laser point cloud is effective in estimating the road adhesion coefficient. Moreover, considering the performance of EHB system, the improved AEB algorithm is deemed more consistent with the practicalities.

A ROBUST AND FRIENDLY HUMAN–ROBOT INTERFACE SYSTEM BASED ON NATURAL HUMAN GESTURES

2010 ◽  
Vol 24 (06) ◽  
pp. 847-866 ◽  
Author(s):  
MING-SHAUNG CHANG ◽  
JUNG-HUA CHOU
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Fuzzy Logic Controller ◽  
Tracking System ◽  
Back Propagation ◽  
Principal Component ◽  
Working Environment ◽  
Expression Recognition ◽  
Linear Discriminant ◽  
Human Gestures

In this paper, we design a robust and friendly human–robot interface (HRI) system for our intelligent mobile robot based only on natural human gestures. It consists of a triple-face detection method and a fuzzy logic controller (FLC)-Kalman filter tracking system to check the users and predict their current position in a dynamic and cluttered working environment. In addition, through the combined classifier of the principal component analysis (PCA) and back-propagation artificial neural network (BPANN), single and successive commands defined by facial positions and hand gestures are identified for real-time command recognition after dynamic programming (DP). Therefore, the users can instruct this HRI system to make member recognition or expression recognition corresponding to their gesture commands, respectively based on the linear discriminant analysis (LDA) and BPANN. The experimental results prove that the proposed HRI system perform accurately in real-time face detection and tracking, and robustly react to the corresponding gesture commands at eight frames per second (fps).

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

scholarly journals EFEKTIVITAS CAPAIAN KOMPETENSI BELAJAR SISWA DALAM PEMBELAJARAN SAINS DI SEKOLAH DASAR

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Yanti Fitria
Keyword(s):  
Elementary School ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Test Results ◽  
Qualitative And Quantitative ◽  
Average Value ◽  
Fourth Class ◽  
Fourth Grade Students ◽  
Teachers And Students ◽  
Inquiry Method

This study aimed to describe effecitivity of the learning outcome IPA using inquiry method. This type of research is the Classroom Action Research (CAR). Research procedures conducted through four stages, namely planning, implementation, observation, and reflection. The study uses a qualitative and quantitative approach. Subjects were teachers and students of fourth class in elementary school 08 Padang City. Data was obtained from the results of observations and test results of learning. The research was conducted by two cycles and each cycle consisting of two meetings. The results of the research that has been conducted every cycle seen an increasein the average value of student learning outcomes are: (a) the cognitive aspects of 75 to 93, (b) the affective aspects of 74 to 95, and (c) psychomotor aspects from 76 to 94. It can be concluded that the CAR using the inquiry method can improve learning outcomes of science fourth grade students of fourth class in the state elementary school 08 Padang City.Key words: Learning competency; science learning; inquiry method

scholarly journals Statistical Analysis for Evaluating Natural Yellow Coloring Agents from Peel of Local Fruits in Malang: Mangosteen, Honey Pineapple and Red Dragon Fruits

2019 ◽  
Vol 1 (2) ◽  
pp. 49 ◽  
Author(s):  
Yehezkiel Steven Kurniawan ◽  
Marcelinus Alfasisurya Setya Adhiwibawa ◽  
Edi Setiyono ◽  
Muhammad Riza Ghulam Fahmi ◽  
Hendrik Oktendy Lintang
Keyword(s):  
Statistical Analysis ◽  
Color Space ◽  
Principal Component ◽  
Ethanolic Extract ◽  
Test Results ◽  
Pca Analysis ◽  
Dragon Fruit ◽  
Yellow Pigments ◽  
Coloring Agents ◽  
Acetone Extracts

In the present work, a comprehensive statistical analysis was performed to evaluate the potential application of peel of local fruits from Malang, i.e. mangosteen, honey pineapple and red dragon fruits for natural yellow coloring agents. The yellow pigments from those fruit peels were extracted through a simple maceration method using distilled water, acetone and ethanol as the solvents. The CIE color space of the extracts was measured to obtain L*, a* and b* values. The obtained data were further analyzed using Principal Component Analysis (PCA), Multivariate Analysis of Variance (MANOVA) and Duncan Test to determine the most potent natural yellow coloring agent. All the extracts were appeared as mild to strong yellow liquid except for acetone extract for the peel of red dragon fruit extracts. From the CIE color space and PCA analysis, either ethanolic or acetone extracts of mangosteen appears as a strong yellow liquid and they are statistically not different. Interestingly, the MANOVA and Duncan test results are able to distinguish that the ethanolic extract of mangosteens’ peel as the best candidate for natural yellow coloring agents because of its lowest L* and also highest b* variable values.

scholarly journals PENINGKATAN KEMAMPUAN MENULIS PARAGRAF DESKRIPTIF SISWA KELAS X SMA NEGERI 02 BENGKULU TENGAH DENGAN MENGGUNAKAN METODE MENULIS BERANTAI (ESTAFET WRITING)

2016 ◽  
Vol 2 (2) ◽  
pp. 157-163
Author(s):  
Heni Puspita
Keyword(s):  
Data Analysis ◽  
Research Data ◽  
Test Results ◽  
Test Cycle ◽  
Test Technique ◽  
Qualitative And Quantitative ◽  
Average Value ◽  
School Year ◽  
Analysis Technique ◽  
Photo Documentation

The purpose of this study for improve the students class X1 skill of SMAN 2 Central Bengkulu Tengah in write descriptive paragraph with estafet writing method. This study design is the design of classroom action research conducted in two cycles, the first cycle and the second cycle. Collecting data on the first cycle and the second cycle using test technique and nontest. The test used is a test action in the form of assignment to write a description, whereas nontest techniques used in the form of guidelines for observation, the journal guidelines, interview guides, and photo documentation guidelines. Data analysis technique of this research is qualitative and quantitative. Quantitative techniques are used to analyze and compare test results pre-cycle, the first cycle, the second cycle, and qualitative techniques used to analyze and compare the results nontest in the first cycle and the second cycle. Based on the analysis of research data, in class X1 totaling 30 students can be concluded that by using the estafet writing method can increase the skill of writing a paragraph descriptive. In the first cycle, the value of an average of 71.65% in the second cycle, the average value of 88.73%, an increase of 17.08%. This means that there is an increase in the skill of writing a paragraph descriptive of the students with estafet writing method. This increase can be seen from the results of tests conducted students in class X SMAN 2 Central Bengkulu 2016/2017 school year that includes the end of the test cycle test cycle I and II.  

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