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This study was to determine the chemical composition, microstructure, and hardness of a new alloy of zircalloy (Zr-Nb-Mo-Y-Sn) for the cladding of nuclear fuel elements. To find out the effect of element Sn on the alloy, the various concentration of Sn was used (Zr-1% Nb -0.7% Mo-2% Y-X% Sn.). The alloy was made by using a single electric arc furnace with a high purity argon gas atmosphere. The melted metal alloys were characterized by optical microscopy, XRD, and Hardness. Optical microscopes were used to examine the microstructure that occurs in the Zr-Nb-Mo-Y-Sn alloy. Based on optical microscope characterization, it was seen that the alloys contained basket weave microstructure. X-ray diffraction confirmed that the main phase of the alloy is α. Based on the microhardness test, it shows that the higher the Sn concentration will increase the hardness value of the alloy.

In the industrial world, mixing operations are very widely used to process raw materials into products such as petroleum, chemicals, and various other types, so increasing the effectiveness of mixing operations is necessary. Usually, in industrial-scale plants, the conventional PID controller is used in the main controllers that are relied upon, but they often underperform, especially when dealing with non-linear systems. In this research, a neural network (NN)-based controller is proposed to overcome that problem. The plant model used for the simulation in this research is a mixing water plant, where the temperature and water level in the mixture tank will be controlled. This plant consists of 2 water flow inputs, namely cold water and hot water, which flows into the mixed tank where the temperature and water level of the mixture will be maintained and controlled according to the desired set points by regulating the flow of cold water and hot water. There are two types of NN-based controllers in this simulation study. The first is NN-based controller which has inputs in the form of the set point (SP), the current step (n) process variable (PV), and the previous step (n-1) process variable, while the second is a NN-based controller with set point (SP), error, and change of error for the inputs. Both of these NN-based controllers were developed using feed-forward neural networks, and the simulation was conducted by using MATLAB/SIMULINK. The simulation results show that the proposed NN-based controllers provide better performances when compared to conventional PID controllers. The best performance is obtained using the NN-based controller that has inputs in the form of set point, error, and change of error, with settling time of 472.7 s and rise time of 242.0 s in controlling the temperature, and settling time of 984.4 s, rise time about 447.6 s in controlling the level, that is faster than the PID controller which has 613.7 s and 393.9 s for settling time and rise time respectively in controlling the temperature, while gives settling time of 3216 s and rise time 412.8 s in controlling the level. Moreover, the NN-based controller produces a system response that has no overshoot at all.

Bladder cancer on a Computed Tomography Scanner (CT-Scan) image has a different shape, location and texture for each image. Each person’s bladder is different in size when the image is taken. Contrast and non-contrast image captured on a CT scan of the bladder can be used to determine the structure and shape of the bladder. However, the difference in contrast images between an abnormality and a healthy bladder is often not visually obvious, making the evaluation is difficult. Although there have been many studies on bladder cancer detection based on CT images that have been carried out, it has been reported that the success rate for detecting bladder cancer is still relatively low. In this study, Computer- Aided Diagnosis (CAD) is used to help evaluating bladder abnormalities using the segmentation method based on an active contour algorithm. The Gray Level Co-Occurrence Matrix (GLCM)-based features of the images are used as the inputs of the Artificial Neural Network (ANN) to classify the normal and abnormal images. The research CAD in this study using MATLAB. A total number of samples were 320 images with 200 abnormal (25 patient), and 120 normal (8 patient) images were used as training and testing data. The result based on Receiver Operating Characteristic (ROC) illustrated that the training accuracy was 90,2 ± 2.68 %, and the test accuracy was 89,2 ± 2,95%. These results mean that this developed CAD system can recognize normal and abnormal bladder images.

Compared to computed tomography (CT), three-dimensional rotational angiography (3DRA) presents tomographic images with relatively higher noise due to a lack of imaging projections and a more considerable scatter volume. This paper extends the use of the residual encoder-decoder convolutional neural network (RED-CNN) to denoise 3DRA images with a specific mode of training, where an in-house algorithm was trained using CT images of homogeneity water phantom as target noise level for 3DRA images to refer to. The algorithm was applied to 3DRA images obtained using GE Optima CL323i with CT images from a Siemens Somatom 16 as benchmarks. All 3DRA images were also denoised using the standard block-matching and 3D filtering (BM3D) method as a preliminary comparison. Noise levels were quantified in Hounsfield unit using ImQuest software, while the limiting spatial resolution (10% modulation transfer function, MTF) was determined using Spice-CT plugin of the ImageJ tool. Whereas resulting 3DRA images denoised using BM3D had an average of 22,6% decrease in noise level (from 29,6 HU to 22,9 HU), our method presents an average of 49,0% decrease in noise level (29,6 HU to 15,1 HU) for the same images. The 3DRA images denoised using RED-CNN underwent an increase of spatial resolution (8,2%), while BM3D-denoised images had their 10% MTF reduced by an average of 8,7%. These preliminary results had indicated a potential use of RED-CNN based method to denoise 3DRA images without compromising the spatial resolution.

Control system process is an important process that occurs in the branch of industrial world, one of which is in the realm of the oil and gas industry in production of the upstream process. One of the main instrument in the upstream oil and gas process is a separator which has the function of separating the fluid content of crude oil which flows through the pipe into several phases. In a three-phase separator, the separator will separate the heavy content of crude oil into three phases, namely the gas, water and oil phases before being distributed to the gathering station. In fact, almost all control processes separator instrument at PT. Pertamina EP still using the conventional PID control model which must be continuously monitored by human resources 24 hours per day. Sometimes also with a manual control system like this causes many factors in the calculation of daily logging data errors. Therefore, this research designed an intelligent system- based control method, which is a neuro-fuzzy control. This neuro-fuzzy control method is designed using Adaptive Neuro- Fuzzy Inference System (ANFIS) algorithm model with input in the form of setpoint, error, and error difference from the process of fluid separator variable, namely fluid level (h). The research was conducted using the Simulink / MATLAB application by entering the transfer function of the separator mathematical model and then making a comparison by looking at the response graph and parameters between the PID and ANFIS controller models. The results of this research conclude that the performance of the ANFIS model controller on average has a much better overshoot than the PID model because it is always close to zero in each set point condition and the ANFIS model has a better error value when the set point is 5 with a difference in error 0.712 instead of the error value of PID controller model.

Because of their great potential, nanofluids are widely studied throughout the world to replace conventional fluids as heat-transfer fluids. The nanofluids have advantages compared to conventional fluids because they have a greater heat transfer coefficient. To obtain a large heat transfer coefficient, a nanofluid has to be stable. Nanofluid from Al2O3 is now widely studied in the world. In this study, the synthesis of Al2O3 nanoparticles added with Ag that forming a composite was carried out. The addition of Ag was to improve the performance of Al2O3 nanofluids. The synthesis was carried out using the hydrothermal method by utilizing urea as an agent. For the synthesis, Al(OH)3 powder obtained from local bauxite was dissolved in HCl. Then, the AgNO3 solution was added to the Al chloride solution with Ag concentrations of 0, 0.1, 0.5, and 1 % by weight. Urea was added to the mixture. After that, the mixture was put into an autoclave and heated at 175 °C for 17 hours. The product was calcined at 500 °C for 2 hours then crushed to be powder. The powder was then analyzed using XRD, FTIR, surface area meter, and TEM. The XRD analyses showed that the nanoparticles crystallized in gamma-alumina with a crystallite size of 7.4-8.6 nm. As confirmed by TEM, the particles have fiber shapes or rod-shaped with 25 nm in diameter and 1.33 micron in length and spherical shape. The Al2O3 powder was dispersed in water to form nanofluids. The zeta potential of the nanofluids was good, with an average zeta potential of 41 mV. It indicates that the nanofluids are stable and suitable for heat transfer.

Ocean acidification is one of the phenomena caused by climate change in the world. Most Ocean acidification (OA) experimental is carried out in a laboratory by creating a simple system. In this research, the seawater in tank is given CO2 gas regulated using a needle valve driven by stepper motors using Proportional-Integral-Derivative (PID) controller. This tank is equipped with a stirrer to accelerate the system’s homogeneity, flow sensor, and pH sensor. Arduino as a system control is tasked to regulate stepper motors, reading and keeping the pH value according to the set point value while recording the changes of pH value occurring in the tank. This research shows that the system can reach a desired set point and maintain it with 1.7 % overshoot. From the experimental results, OA system has a good response with a final pH value of 7±0.09 that can also be concluded this system is very suitable for long-term research.

Breast cancer is one of the most common cancer among women and the survival rate tends to be low when its stage found high when treated. To improve breast cancer survival, early detection is critical. There are two ways of detection for breast cancer: early diagnosis and screening. To make an accurate diagnosis in the early stage of breast cancer, the appearance of masses and micro-calcifications on the mammography image are two important indicators. It is time-consuming and challenging to identify micro-calcification from mammogram images by the human eye because of its size and appearance. Several Computer-Aided Detection (CADe) have been developed to support radiologists because the automatic detection of micro-calcification is important for diagnosis and the next recommended treatment. Most of the current CADe systems at this time started using Convolutional Neural Network (CNN) to implement the micro-calcification detection in mammograms and their quantitative results are very satisfying, the average level of accuracy is more than 90%. However, most of the methods used are image fragments from a complete image which are then included in the program. This research conducts an automated approach to detect the location of any micro-calcification in the mammogram images with the complete image and in a simple way. At first, the image preprocessing algorithms were applied to enhance the image quality. After that, the micro-calcification region was labeled using image segmentation based on the Radiologist's expertise. The positive label which contains micro-calcification pixels was taken to train with segmentation network. A total of 354 images from INbreast dataset were used in this research study. Finally, the trained network was utilized to detect the micro-calcification area automatically from the mammogram images. This process can help as an assistant to the radiologist for early diagnosis and increase the detection accuracy of the micro-calcification regions. The proposed system performance is measured according to the error values of Mean Squared Logarithmic Error (MSLE) as the technique to find out the difference between the values predicted by the proposed model and the actual values. The best MSLE loss value obtained was achieved in 0.05 with accuracy 0.95.

An inhouse Electroencephalography (EEG) data acquisition system based on Raspberry Pi and Analog Front End (AFE) ADS1299 EEGFE-PDK has been developed. The system displayed a relative power ratio (RPR) and brain symmetry index (BSI) in real-time. The features of the AFE are simultaneous sampling for eight channels, 24 bits resolution, low power, and low noise of < 5 mW and < 1 μV, respectively. The system consists of 4 units AFE in daisy chain configuration. The communication between AFE and Raspberry Pi is programmable using registers of RDATA format accessed via a serial peripheral interface (SPI) and programmed using C. The data acquired were processed using MATLAB. These data were transferred using Local Area Networking (LAN) filtered based on 5th order Butterworth and processed in a personal computer (PC). The RPR was calculated using Fast Fourier Transforms (FFT) and Power Spectral Density (PSD). The BSI was calculated using Welch method. The acquired and processed data would be sent to High Definition Multiple Interface (HDMI) if needed by users. This system has been evaluated using EEG simulator (NETECH MiniSim EEG), which is generate sinusoidal electrical signal with frequency 2 Hz, 5 Hz, and voltage amplitude 30, 50 μV, with error average less than 6%.

A Magnetic Induction Tomography (MIT) system, one of tomography systems with non-invasive modality, which uses magnetic field induction to reconstruct the distribution of electrical conductivity in an object material has been developed. MIT is a low resolution tomography technique based on the rate of changes in the magnetic flux due to the test material with respect to the main alternating magnetic flux. In this study, the MIT system was designed using 12 receiver and transmitter coils, with an excitation signal in the form of a sinusoidal electrical signal with amplitude of 5 V, frequency of 2 MHz. The coil used has 100 turns of copper wire, with inner diameter of 15.5 mm, and length of 4 cm. Coils are arranged in a circle with an inner diameter of 12 cm and each coil forms a 30 degree angle to each other. The magnitude of the induced electrical voltage at the receiving coil that is influenced by the rate of change of magnetic flux on the transmitter coil, is also affected by the permeability of the medium between the transmitter and receiver coil. The induction electrical voltage on the receiving coils are measured by the MIT data acquisition system, and then the image reconstruction process is carried-out based on those data. This MIT system has been tested by placing an object material in the form of an empty cylindrical iron and also an empty box-shaped iron. This research has been able to get the 2D reconstruction image in accordance with the actual shape of the test material object but not yet perfect.

Stroke diminishes someone’s quality of life since post-stroke symptoms can make some limbs do not function normally-in most cases, feet-thus the stroke patients’ mobility will be limited. However, the brain wave of stroke patients was observed as normal, thus in this study, the normal brainwaves were utilized to rehabilitate post-stroke patient and were expected to help their mobility through wheelchairs with brain waves control so that post-stroke patients can improve their quality of life. Brain Computer Interfaces (BCI) is a technology that allows taking action on a computer based on brain waves. Brain waves are recorded by electroencephalography so they can be processed by a computer. There have been many studies using BCI including analyzing brain waves in humans, many things that can be utilized using BCI make a lot of researchers use them to make smart wheelchairs that use brain control. and developments continue to be made to create the most optimal system. In this project we use one of the topics that is still being developed, that is Motor Imagery, where we record and analyze the brain waves while imagining motor activities such as moving hands, walking, running and so on. This record will serve as data reference to trigger the process on the computer to move the actuator. The purpose of this project is to control the wheelchair based on motor movements obtained by BCI using the Neurosky Mindwave Mobile 2 headset. This headset has one electrode where the signals from one electrode are analyzed by concentration and meditation values from the case of imagery motors, which in this project are more portable than using conventional EEG data acquisition devices that are not portable and use many channels. This headset is able to record data wirelessly via Bluetooth to a PC (Personal Computer), so the obtained signal can be processed and classified into five movement classes using the Matlab GUI, where the classes are default/motionless, move forward, move backward, turn right and turn left. The method used for the wheelchair was replacement of the default joystick in the electric wheelchair with an self-made controller module which was based on brain waves signals obtained from the headset will be processed and classified by Matlab GUI and then forwarded to Arduino Uno to control the motor in the wheelchair. The average success rates of the five classes from five trials were: the first class with a success rate of 82.22 %, the second class with a success rate of 70 %, the third class with a success rate of 73.33 %, the fourth class with a success rate of 46.67 % and the fifth class with a success rate of 17.78 %. The results of this study indicate that Neurosky Mindwave mobile 2 headset can be a possible choice for this project.

Locked-in syndrome (LIS) has to be aware of, even though it is rare in the world. However, this disease has been reported to have ten years survival rates as high as 80 %. The physical appearance of the syndrome is a total paralysis but with preservation of consciousness. Consciousness indicates that the patient’s brain is still able to work quite well. Brain Computer Interfaces (BCI) is a technology that allow users to directly have control on computers or external devices by focusing on which action the users want to do through their brainwaves, which are recorded by the means of electroencephalograph. In this paper, we combine a feature of SDK community edition from a commercial BCI device called Emotiv EPOC+, which is the most credible and cost-effective mobile EEG headset on the market, with UNITY3D, the world’s leading content-creation engine, to build a beautiful and engaging content in Visual Request System to enable communication between patients with LIS and the people around them. In Visual Request System, there are 2 categories of requests, which are internal request system and external request system. Both requests depend on where the request came from. We tested the system and EPOC Control Panel on 10 volunteers to get the success rate data, and we make questionnaire for comparing user-friendly interface between a Visual Request System and Cognitiv Suite in the EPOC Control Panel software from Emotiv and we also ask them about the comfort when using Emotiv EPOC+. Then, we record every calibration data of Cognitiv suite EPOC Control Panel in Xavier TestBench 3.1.18, after that we analyze what happen in calibration data using Power Spectral Density with time window in 10 seconds duration. The results of our research conclude that the Visual Request System built on UNITY3D and Emotiv EPOC+ can be used as a communication system to help LIS patients by making some improvements and the Visual Request System is more user-friendly than EPOC Control Panel to show communication message. Power Spectral Density with time window in 10 second duration analysis indicates that the signal received by the Emotiv only based on an 8 seconds calibration is intersected to one another signal. Therefore, increasing the number of mental states that must be detected by the system will make the system more difficult to distinguish each mental state signal.

Mental health issue is growing rapidly in these recent years. Teenagers and young adult aged of 16–30 years old are the most common victims. Mental health is a really serious issue concerning emotional health. One of the causes on emotional health issues is a lack of self-awareness, which is the key cornerstone on maintaining emotional state. In this study, EEG Neurostyle of 24 channels was used to obtain brain electrical signals. The mental emotions of the subjects were obtained from their reactions due to a set of audio-visual stimuli of approximately 5 minutes, the subject consists of 6 subjects aged 18–22 years old. The expressions of the subjects were recorded EEG signals separately to ensure their emotion according to the source (i.e. sad clips resulting sad emotion). The signals were processed using DFT and PSD to extract their features. The features were used to classify the emotions into 4 classes: happy, sad, scared, and disgust. In this study, the k-NN as classifier was used and obtained training and testing accuracy for all the features were greater than 52 % and 30 % for 4 classes.

Magnetoacoustic Tomography (MAT) is one of tomographic methods that utilizes acoustic signals (ultrasonic) produced by the interaction of an external magnetic field with electrons in an object’s material to produce an image that describes the distribution of electrical conductivity in the object’s material. MAT combines good contrast from electrical impedance tomography techniques and good spatial resolution from sonography. In this research study, a MAT system has been designed, with a method of injecting electric current that varies with time, into the object material under the influence of an external static magnetic field, or often called Magnetoacoustic Tomography with Current Injection (MAT-CI). Because the electric current is injected from the outside of the sample to be reconstructed, the sample used as an object must be conductive. To investigate the ability of the system that has been built, this experiment used various types of material with various shapes as samples. Overall, the system created can acquire the ultrasonic signal generated by the sample and this system is able to reconstruct the test sample shape image with the help of k-Wave tomography reconstruction software, which is a Matlab-based magneto/photo acoustic tomography image reconstruction software.Magnetoacoustic Tomography (MAT) is one of tomographic methods that utilizes acoustic signals (ultrasonic) produced by the interaction of an external magnetic field with electrons in an object’s material to produce an image that describes the distribution of electrical conductivity in the object’s material. MAT combines good contrast from electrical impedance tomography techniques and good spatial resolution from sonography. In this research study, a MAT system has been designed, with a method of injecting electric current that varies with time, into the object material under the influence of an external static magnetic field, or often called Magnetoacoustic Tomography with Current Injection (MAT-CI). Because the electric current is injected from the outside of the sample to be reconstructed, the sample used as an object must be conductive. To investigate the ability of the system that has been built, this experiment used various types of material with various shapes as samples. Overall, the system create...