KONTROL FUZI PADA WAKTU PENGAPIAN MOTOR OTTO (Fuzzy Logic Control for Spark Advance of Otto Engine)

The problem of detonation (knocking) in the internal c bustion engines, especially in the Otto (petrol) engine, that makes som damages, low fuel economy and performance. The detonation can be cause by many things, such as: high compression ratio, low grade fuel, bad combustion camber, low turbulence, large spark advance (timing). Governor and vacuum control Spark timing in the conventional ignition system. It is reliable mechanism, cannot work properly at all conditions. Most of them make detonation occur at low speed and low endurance of the contact breaker. The last technology, electronical device with detonation sensor, replaces the conventional system. The fuzzy logic control can control the detonation by king correction of the spark advance (ignition timing) automatically in all condition of speed and load (throttle). This control is integration of conventional and electronical system of micro controller

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

Detection and Identification of Detonation Sounds in an Internal Combustion Engine using Wavelet and Regression Analysis

Improving efficiency and power in an internal combustion engine is always impeded by detonation (knock) problems. This detonation problem has not been explained fully yet. Quick and accurate detection of detonation is also in the development stage. This research used a new method of detonation sound detection which uses microphone sensors, analysis of discrete wavelet transform (DWT), and analysis of the regression function envelope to identify the occurrence of detonation. The engine sound was captured by the microphone; it was recorded on a computer; it was proceeded using a DWT decomposition filtering technique; it was then subjected to normalization and regression function envelope to get the shape of the wave pattern for the vibration. Vibrational wave patterns were then compared to a reference using the Euclidean distance calculation method, in order to identify and provide an assessment decision as to whether or not detonation had occurred. The new method was applied using Matlab and it has yielded results which are quite effective for the detection and identification of detonation and it is also capable of producing an assessment decision about the occurrance of detonation

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 %

Detection and Identification of Detonation Sounds in an Internal Combustion Engine Using Wavelet and Regression Analysis

Improving efficiency and power in an internal combustion engine is always impeded by detonation (knock) problems. This detonation problem has not been explained fully yet. Quick and accurate detection of detonation is also in the development stage. This research used a new method of detonation sound detection which uses microphone sensors, analysis of discrete wavelet transform (DWT), and analysis of the regression function envelope to identify the occurrence of detonation. The engine sound was captured by the microphone; it was recorded on a computer; it was proceeded using a DWT decomposition filtering technique; it was then subjected to normalization and regression function envelope to get the shape of the wave pattern for the vibration. Vibrational wave patterns were then compared to a reference using the Euclidean distance calculation method, in order to identify and provide an assessment decision as to whether or not detonation had occurred. The new method was applied using Matlab and it has yielded results which are quite effective for the detection and identification of detonation and it is also capable of producing an assessment decision about the occurrance of detonation

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. ================================================================================================ 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

Rancang Bangun Mini Smart Greenhouse Hidroponik Tipe Rakit Apung Berbasis IoT untuk Memenuhi Kebutuhan Praktikum di Laboratorium Teknik Tata Air

Kebutuhan mini smart greenhouse yang berbasis IoT di sebuah laboratorium sangat penting guna meningkatkan pemahaman mahasiswa terkait perkembangan teknologi terkini. Oleh karena itu perlu adanya inovasi dalam mengikuti perkembangan pembelajaran pendidikan vokasi yang telah berjalan pada era 4.0. Tujuan penelitian ini yaitu merancang mini smart greenhouse dengan hidroponik tipe rakit apung berbasis IoT. Penelitian dimulai dari tahapan perencanaan dan perancangan bangunan yang meliputi konstruksi, mekanik, elektronik, dan program arduino uno. Hasil penelitian adalah rancang bangun mini smart greenhouse berbasis IoT sistem hidroponik Rakit Apung yang dilengkapi dengan evaporatitive cooling pad system. Evaporative cooling pad sistem dilengkapi dengan fan yang berfungsi untuk menjaga suhu dan kelembaban di dalam mini smart greeenhouse.  Arduino  uno berfungsi sebagai pengatur suhu dan kelembapan di dalam mini smart greenhouse. Tanaman yang dipilih adalah selada hijau keriting,  tanaman selada memiliki karakteristik tumbuh pada suhu 25 oC – 28 oC dan kelembapan 65 % - 78 %. Apabila kelembapan di dalam mini smart greenhouse < 65% maka secara otomatis pompa air dan fan hidup. Air yang ditampung di bagian bawah cooling pads mengalir membasahi seluruh permukaan cooling pads dan uap udara dihisap fan. Apabila kelembapan > 78% maka secara otomatis cooling pads dan fan berhenti beroprasi. Hasil penelitian menunjukkan bahwa alat pengendali suhu dan kelembapan menggunkan arduino uno dapat bekerja dengan bai

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