5 research outputs found
Asthma Identification Using Gas Sensors and Support Vector Machine
The exhaled breath analysis is a procedure of measuring several types of gases that aim to identify various diseases in the human body. The purpose of this study is to analyze the gases contained in the exhaled breath in order to recognize healthy and asthma subjects with varying severity. An electronic nose consisting of seven gas sensors equipped with the Support Vector Machine classification method is used to analyze the gases to determine the patient's condition. Non-linear binary classification is used to identify healthy and asthma subjects, whereas the multiclass classification is applied to recognize the subjects of asthma with different severity. The result of this study showed that the system provided a low accuracy to distinguish the subjects of asthma with varying severity. This system can only differentiate between partially controlled and uncontrolled asthma subjects with good accuracy. However, this system can provide high sensitivity, specificity, and accuracy to distinguish between healthy and asthma subjects. The use of five gas sensors in the electronic nose system has the best accuracy in the classification results of 89.5%. The gases of carbon monoxide, nitric oxide, volatile organic compounds, hydrogen, and carbon dioxide contained in the exhaled breath are the dominant indications as biomarkers of asthma.The performance of electronic nose was highly dependent on the ability of sensor array to analyze gas type in the sample. Therefore, in further study we will employ the sensors having higher sensitivity to detect lower concentration of the marker gases
Electronic Nose using Gas Chromatography Column and Quartz Crystal Microbalance
The conventional electronic nose usually consists of an array of dissimilar chemical sensors such as quartz crystal microbalance (QCM) combined with pattern recognition algorithm such as Neural network. Because of parallel processing, the system needs a huge number of sensors and circuits which may emerge complexity and inter-channel crosstalk problems. In this research, a new type of odor identification which combines between gas chromatography (GC) and electronic nose methods has been developed. The system consists of a GC column and a 10-MHz quartz crystal microbalance sensor producing a unique pattern for an odor in time domain. This method offers advantages of substantially reduced size, interferences and power consumption in comparison to existing odor identification system. Several odors of organic compounds were introduced to evaluate the selectivity of the system. Principle component analysis method was used to visualize the classification of each odor in two-dimensional space. This system could resolve common organic solvents, including molecules of different classes (aromatic from alcohols) as well as those within a particular class (methanol from ethanol) and also fuels (premium from pertamax). The neural network can be taught to recognize the odors tested in the experiment with identification rate of 85 %. It is therefore the system may take the place of human nose, especially for poisonous odor evaluations.
Klasifikasi Kualitas Pisau Potong Tembakau (CUT CELL) Menggunakan Metode Radial Basis Function (RBF)
Indonesia is one of countries that produces several types of tobacco. Almost 80% tobacco produces is used of cigarette industry. Tobacco leaves slicing into small cuts is one of the process of cigarette production. The cutting process of tobacco requires Cut Cell which is able to cut tobacco into small pieces. Contol is required in the process of making cut cell to set the quality of the blade. The quality control often has problem in determining the Cut Cell quality. The problem is the length of time needed in determining the quality. In this fast paced era, the Quality Control is demanded to be able to determine the cut cell quality quickly and accurately. To support this need from the Quality Control, a system that can be used to determine the cut cell quality which has fast output result. The research process is started with collecting the system needs, followed by system designing, then system making, and system test. The system designing is initiated by preparing the test data and training data which are going to be used for the making and testing of the system. RADIAL BASIS FUNCTION consist of several calculation processes. The first process is the process of center search of each variable using K-MEANS method. Aftar the center is found, the deviation standard of each variable is calculated. The second process is setting the GAUSSIAN matrix of every group found. The third process is the process of new weight and bias values search by doing pseudo-inverse GAUSSIAN matrix multiplication. The forth process is classification in which this process sets out the classication result by multiplying the value of GAUSSIAN matrix and new weight and bias applying network output formula. The experiment done to 75 experiment data which are compared to manual data as the reference result 12 different data, thus it can be concluded that the accuracy level of this system is 84 %
Analisa Udara Pernapasan Menggunakan Deret Sensor Gas Dan Support Vector Machine Untuk Klasifikasi Asma Menurut Derajad Keparahan
Analisis udara pernapasan yang dihembuskan (disingkat: analisis napas)
adalah metode non-invasif untuk mendapatkan informasi mengenai keadaan klinis
seseorang dengan mendeteksi dan mengukur gas-gas dan senyawa organik volatil
yang ada didalam napas. Perkembangan utama dalam teknologi pemantauan medis
dan metode diagnosa adalah berdasarkan analisa darah dan urin. Diagnosa
berdasarkan analisa napas relatif kurang berkembang dan belum banyak digunakan
dalam praktik klinis. Disertasi ini mengkaji tentang deteksi dan identifikasi napas
yang bersumber dari pasien / subyek asma dan subyek sehat menggunakan electronic
nose (e-nose). Penetapan terhadap subyek asma atau subyek sehat ditentukan dengan
cara klasifikasi menggunakan metode klasifikasi.Dalam proses penelitian disertasi
ini, kami mengawali penelitian menggunakan e-nose dengan deret sensor resonator
kuarsa yang dilapisi bahan polimer. Obyek penelitian adalah gas/uap dari cairan yang
mudah menguap seperti alkohol, bensin dan lainnya. Klasifikasi pola menggunakan
metode Support Vector Machine (SVM). Hasil pengujian menunjukkan tingkat
konsistensi (reliability) e-nose dalam pengujian berulang untuk obyek yang sama
menunjukkan tingkat reliabilitas kuat. Hasil klasifikasi baik sekali dengan tingkat
akurasi rata-rata 95%. Setelah itu dilakukan penelitian utama menggunkan e-nose
dengan deret sensor metal oxide semiconductor (MOS). Obyek penelitian adalah
gas/uap dari udara pernapasan yang dihembuskan. Analisa sebaran data
menggunakan metode Standar Deviasi dan Principle Component Analysis (PCA).
Seleksi fitur terbaik menggunakan Algoritma Genetika. Klasifikasi menggunakan
metode SVM. Hasil analisa sebaran data menunjukkan tingkat heterogen data cukup
tinggi. Hasil seleksi fitur terbaik menunjukkan bahwa jumlah sensor pada deret
sensor dapat dikurangi, dengan akurasi tetap pada tingkat cukup baik. Hasil
klasifikasi menunjukkan akurasi yang baik untuk mengidentifikasi subyek sehat dan
asma, tetapi kurang baik untuk mengidentifikasi subyek asma dengan tingkat
keparahan berbeda.
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Exhaled breath air analysis (abbreviated as breath analysis) is a non-invasive method
for obtaining information about a person's clinical condition by detecting and measuring the
gases and volatile organic compounds in the breath. The main developments in medical
monitoring technology and diagnostic methods are based on analysis of blood and urine.
Diagnosis based on breath analysis is relatively underdeveloped and has not been widely used
in clinical practice. This dissertation examines the detection and identification of breath
originating from patients / asthma subjects and healthy subjects using electronic nose (e-
nose). Determination of asthma subjects or healthy subjects is determined by classification
using the classification method. In the process of this dissertation research, we begin the
research using e-nose with a series of quartz resonator sensors coated with polymer material.
The object of research is gas / vapor from volatile liquids such as alcohol, gasoline and
others. Pattern classification uses the Support Vector Machine (SVM) method. The test
results show the e-nose reliability in repeated testing for the same object shows a strong level
of reliability. The classification results are very good with an average accuracy rate of 95%.
After that the main research was conducted using the e-nose with a series of metal oxide
semiconductor (MOS) sensors. The object of research is gas / vapor from exhaled breathing
air. Data distribution analysis uses the Standard Deviation method and the Principle
Component Analysis (PCA). The best feature selection uses Genetic Algorithms.
Classification using the SVM method. The results of the data distribution analysis show a
fairly high heterogeneous level of data. The best feature selection results show that the
number of sensors in the sensor array can be reduced, with accuracy remaining at a fairly
good level. The classification results show good accuracy for identifying healthy subjects and
asthma, but it is not good for identifying asthma subjects with different severity