Plume Detection System Based Internet of Things

Security is one of the important aspects in a system or environment. Residential, office, tourist and industrial areas are places that are prone to fires because they contain flammable objects. Slow handling when a gas leak occurs can trigger a fire. The solution that can be used to minimize the occurrence of fires is to build tools that work to monitor the condition of the room or environment that is prone to leakage of gas or other flammable liquids. The design and manufacture of a system to detect LPG and alcohol gas leaks can be useful for providing information in the event of a gas or alcohol leak so that it can be handled quickly and minimize fire damage. This system combines an plume detection system with an internet of things system so that it can provide information when a gas or flammable liquid leak occurs. The gas leak information is sent as a notification to the telegram from the operator. The design and manufacture of this system uses the Waterfall methodology with the following stages: analyzing (covering the need for system creation), system design (including designing electronic circuits and web monitoring interfaces), implementing system design and testing the system as a whole. The result of this research is that an electronic detection system has been successfully built that can distinguish gases and can provide information via telegram and web if gas is detected in the sensor environment. In the LPG gas leak test, the results show that the characteristics of LPG gas, namely the sensor output voltage, have an average of 4.17 volts with an average Part Per Million (PPM) of 8340 and the characteristics of alcohol gas, namely the sensor output voltage, have an average of 0, 13 volts with an average Part Per Million (PPM) of 254

Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system. Hence, an electronic nose system is required for the gas classification process. This study presents the design of electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In this study, gas samples including methanol, acetonitrile, and benzene are used for system performance measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into two processes i.e., the training process and the external validation process. According to the result performance, the training process has the accuracy of 98.7% and the external validation process has the accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the gas samples

Effective discrimination of flavours and tastes of Chinese traditional fish soups made from different regions of the silver carp using an electronic nose and electronic tongue

Silver carp is a one of the most important freshwater fish species in China, and is popular when making soup in the Chinese dietary culture. In order to investigate the profile of fish soup tastes and flavours cooked using different regions of the same fish, the silver carp was cut into four different regions: head, back, abdomen, and tail. The differences in taste and flavour of the four kinds of homemade fish soup were investigated by an electronic nose and electronic tongue. The basic chemical components of the different fish regions and the SDS-PAGE profile of the fish soup samples were investigated. Two chemometrics methods (principal component analysis and discriminant factor analysis) were used to classify the odour and taste of the fish soup samples. The results showed that the electronic tongue and nose performed outstandingly in discriminating the four fish soups even though the samples were made from different regions of the same fish. The taste and flavour information of different regions of the silver carp fish could provide the theoretical basis for food intensive processing

Discriminant Analysis as a Tool for Detecting the Acoustic Signals of Termites Coptotermes Curvignathus (Isoptera: Rhinotermitidae)

Various methods for termite detection have been developed, one of them is purely based on the acoustic signal. However, that method still has a weakness, wherein it is difficult to separate the signal generated by the termite or the noise from the environment. The combination of the feature extraction at the acoustic signal and the classification model is expected to overcome the weakness. In this investigation, we inserted 220 subterranean termites Coptotermes curvignathus into pine wood for feeding activity and observed its acoustic signal. In addition, three acoustic features (i.e., short-term energy, entropy and zero moment power) were proposed to recognize the termite's acoustic signal. Subsequently, these features will be analyzed and combined with discriminant analysis to produce the robust classification model. According to the numerical results, the integrated discriminant analysis and the acoustic feature in our termite detection system has an 83.75% accuracy

Efficient android electronic nose design for recognition and perception of fruit odors using Kernel Extreme Learning Machines

This study presents a novel android electronic nose construction using Kernel Extreme Learning Machines (KELMs). The construction consists of two parts. In the first part, an android electronic nose with fast and accurate detection and low cost are designed using Metal Oxide Semiconductor (MOS) gas sensors. In the second part, the KELMs are implemented to get the electronic nose to achieve fast and high accuracy recognition. The proposed algorithm is designed to recognize the odor of six fruits. Fruits at two concentration levels are placed to the sample chamber of the electronic nose to ensure the features invariant with the concentration. Odor samples in the form of time series are collected and preprocessed. This is a newly introduced simple feature extraction step that does not use any dimension reduction method. The obtained salient features are imported to the inputs of the KELMs. Additionally, K-Nearest Neighbor (K-NN) classifiers, the Support Vector Machines (SVMs), Least-Squares Support Vector Machines (LSSVMs), and Extreme Learning Machines (ELMs) are used for comparison. According to the comparative results for the proposed experimental setup, the KELMs produced good odor recognition performance in terms of the high test accuracy and fast response. In addition, odor concentration level was visualized on an android platform.TUBITA

Identifikasi Kualitas Ikan menggunakan Deret Sensor Elektrokimia dan Neural Network

Identifikasi kualitas ikan digunakan untuk menentukan busuk tidaknya ikan sehingga diketahui bahwa apakah ikan tersebut layak untuk dikonsumsi. Namun saat ini, cara mengetahui ikan tersebut layak dikonsumsi atau tidak hanya menggunakan pengamatan lewat fisik dan bau, secara langsung oleh manusia, sehingga bisa membahayakan manusia dikarenakan ikan yang sudah busuk memiliki gas-gas yang beracun serta berbau menyengat yang dihasilkan oleh mikroorganisme di makanan tersebut. Untuk mengatasi hal tersebut, dibutuhkan suatu alat yang dapat mengenali kualitas ikan sehingga tidak perlu ada pengamatan secara langsung oleh manusia untuk mengenali kualitas ikan yang layak dikonsumsi atau tidak. Pada tugas akhir ini, dirancang alat untuk mengidentifikasi kualitas ikan dari nilai gas H2S, NH3, dan CO dimana gas-gas tersebut akan dideteksi menggunakan deret sensor elektrokimia untuk membaca gas yang akan dikonversi menjadi sinyal listrik analog. Listrik analog tersebut akan dibaca oleh Analog to Digital Converter 16 bit berupa modul ADS1115 dan data akan dikonversi menjadi nilai ppm (part per million). Data dari output sensor tersebut akan dijadikan bahan proses learning untuk jaringan saraf tiruan (Neural Network) yang akan digunakan untuk melakukan klasifikasi kualitas dari ikan yang dilakukan menggunakan mikrokontroller Arduino Due. Alat yang dirancang telah mampu melakukan klasifikasi kualitas beragam jenis ikan utuh yang dijual dipasaran dengan ukuran 100-200 gram dengan tingkat akurasi sebesar 80%. =================================================================================================================================== Identification of the quality of fish is used to determine whether or not the fish rot is known so that it is suitable for consumption. But today, the identification for fish quality is using observation through physical and odor directly by humans. This kind of activity may be dangerous because of the poisonous and pungent gases produced by microorganism in rotten fish. In order to prevent this, a tool that can identify the quality of fish is needed so there is no need direct observation to be done by humans to identy the quality of fish. In this final project, a tool is designed to identify the quality of fish from the value of H2S, NH3, and CO gas where the gases will be detected using gas array sensor that will be converted into an analog electrical signal. Analog signal will be read by Analog to Digital Converter 16 bit ADS1115 module and data will be converted to ppm (part per million) value. Gas data obtained by sensor output will be used as material for the learning process of artificial neural network that will be used to classify the quality of fish in Arduino Due microcontroller. The designed tool has been able to classify the quality of various types of whole fish which are being sold in market with a size of 100-200 grams with an accuracy rate of 80%

Identifikasi Kualitas Susu Sapi dengan Menggunakan Deret Sensor Gas dan Potensiometri dengan Metode Neural Network

Identifikasi kualitas susu merupakan sebuah upaya untuk menggolongkan kondisi susu sapi yang akan dikonsumsi. Dalam identifikasi kualitas susu membutuhkan proses pengecekan laboratorium dengan waktu lama. Pengenalan tersebut dapat diketahui dengan melihat mikroorganisme yang umum ditemukan dalam susu. Selain itu dapat juga langsung dideteksi dengan menggunakan hidung dan lidah. Namun, ini berbahaya karena dapat mempengaruhi kesehatan manusia. Selain itu, indra manusia memiliki sensitivitas yang berbeda dan tidak akurat dalam mendeteksi kualitas susu. Pada penelitian ini telah mengembangkan sensor untuk mengidentifikasi kualitas susu. Peran hidung manusia diganti dengan deret sensor gas yang bertujuan untuk identifikasi dari aroma susu. Sedangkan peran lidah diganti dengan deret sensor potensiometri untuk identifikasi rasa atau senyawa dalam susu. Output sensor gas dan potensiometri akan menjadi masukan bagi neural network. Fungsi neural network ini adalah untuk mengidentifikasi kualitas susu dengan cara dilatih terlebih dahulu. Hasil penelitian ini dapat menghasilkan pola yang berbeda terhadap sampel susu yaitu susu segar, basi, dan sangat basi. Hasil identifikasi menggunakan neural network memiliki tingkat keberhasilan 83%. Penelitian ini diharapkan dapat digunakan untuk menilai kualitas susu dengan cepat, mudah dan akurat. ======================= Identification of milk quality is an attempt to classify the condition of cow's milk to be consumed. Currently, the identification of milk quality requires laboratory tests which is time-consuming. This is due to the identification of milk quality by analyzing the microorganisms commonly found in milk. In addition, milk quality can be directly detected by using the human nose and tongue. However, this is harmful because it can affect the human health. In addition, the human senses have a different sensitivity that is not accurate in detecting the quality of milk. In this study has developed a sensor system to assess the quality of milk. The role of the human nose is replaced by gas sensor array for the identification of the smell or odor of milk. While the tongue is taken over by a potentiometric sensor array for identification of taste or compounds in the milk. The output of the gas sensors and potensiometric sensors become input for the neural network. The function of this neural network to identificaion of milk quality by way of being trained first. The experimental result shows that this sensor array can produce different patterns to the fresh, sour, and spoiled milk samples. The Neural Network can be used to assess the quality of milk with a success rate of 83%. This technique is expected to be used as a tool to assess the quality of milk quickly, easily, and accurately