Comparison of the utilization of 110 °C and 120 °C heat sources in a geothermal energy system using Organic Rankine Cycle (ORC) with R245fa, R123, and mixed-ratio fluids as working fluids

© 2019 by the authors. Binary cycle experiment as one of the Organic Rankine Cycle (ORC) technologies has been known to provide an improved alternate scenario to utilize waste energy with low temperatures. As such, a binary geothermal power plant simulator was developed to demonstrate the geothermal energy potential in Dieng, Indonesia. To better understand the geothermal potential, the laboratory experiment to study the ORC heat source mechanism that can be set to operate at fixed temperatures of 110 °C and 120 °C is conducted. For further performance analysis, R245fa, R123, and mixed ratio working fluids with mass flow rate varied from 0.1 kg/s to 0.2 kg/s were introduced as key parameters in the study. Data from the simulator were measured and analyzed under steady-state condition with a 20 min interval per given mass flow rate. Results indicate that the ORC system has better thermodynamic performance when operating the heat source at 120 °C than those obtained from 110 °C. Moreover, the R123 fluid produces the highest ORC efficiency with values between 9.4% and 13.5%

A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

The progress of today's technology is growing very quickly. This becomes the motivation for the community to be able to continue and provide innovations. One technology to be developed is the application of brain signals or called with electroencephalograph (EEG). EEG is a non-invasive measurement method that represents electrical signals from brain activity obtained by placement of multiple electrodes on the scalp in the area of the brain, thus obtaining information on electrical brain signals to be processed and analyzed. Lie is an act of covering up something so that only the person who is lying knows the truth of the statement. The hidden information from lying subjects will elicit an EEG-P300 signal response using Independent Component Analysis (ICA) in different shapes of amplitude that tends to be larger around 300 ms after stimulation. The method used in the experiment is to invite subject in a card game so that the process can be done naturally and the subject can well stimulated. After the trials there are several results almost all subjects have the same frequency on the frequency of 24-27 Hz. This is a classification of beta waves that have a frequency of 13-30 Hz where the beta wave is closely related to active thinking and attention, focusing on the outside world or solving concrete problems

Progress in development of nanostructured manganese oxide as catalyst for oxygen reduction and evolution reaction

The rise in energy consumption is largely driven by the growth of population. The supply of energy to meet that demand can be fulfilled by slowly introducing energy from renewable re-sources. The fluctuating nature of the renewable energy production (i.e., affected by weather such as wind, sun light, etc.), necessitates the increasing demand in developing electricity storage systems. Reliable energy storage system will also play immense roles to support activities related to the internet of things. In the past decades, metal‐air batteries have attracted great attention and interest for their high theoretical capacity, environmental friendliness, and their low cost. However, one of the main challenges faced in metal‐air batteries is the slow rate of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) that affects the charging and the discharging performance. Various types of nanostructure manganese oxide with high specific surface area and excel-lent catalytic properties have been synthesized and studied. This review provides a discussion of the recent developments of the nanostructure manganese oxide and their performance in oxygen reduction and oxygen evolution reactions in alkaline media. It includes the experimental work in the nanostructure of manganese oxide, but also the fundamental understanding of ORR and OER. A brief discussion on electrocatalyst kinetics including the measurement and criteria for the ORR and the OER is also included. Finally, recently reported nanostructure manganese oxide catalysts are also discussed

Deep convolutional neural network with 2D spectral energy maps for fault diagnosis of gearboxes under variable speed.

For industrial safety, correct classification of gearbox fault conditions is necessary. One of the most crucial tasks in data-driven fault diagnosis is determining the best set of features by analyzing the statistical parameters of the signals. However, under variable speed conditions, these statistical parameters are incapable of uncovering the dynamic characteristics of different fault conditions of gearboxes. Later, several deep learning algorithms are used to improve the performance of the feature selection process, but domain knowledge expertise is still necessary. In this paper, a combination domain knowledge analysis and a deep neural network is proposed. By using the input acoustic emission (AE) signal, a two-dimensional spectrum energy map (2D AE-SEM) is created to form an identical fault pattern for various speed conditions of gearboxes. Then, a deep convolutional neural network (DCNN) is proposed to investigate the detailed structure of the 2D input for final fault classification. This 2D AE-SEM offers a graphical depiction of acoustic emission spectral characteristics. Our proposed system offers vigorous and dynamic classification performance through the proposed DCNN with a high diagnostic fault classification accuracy of 96.37% in all considered scenarios

ELECTROMYOGRAPHY GAIT TEST FOR PARKINSON DISEASE RECOGNITION USING ARTIFICIAL NEURAL NETWORK CLASSIFICATION IN INDONESIA

Diagnosa Parkinson Disease (PD) di Indonesia dilakukan secara klinis. Analisa langkah jalan dengan menggunakan sensor electromyography (EMG) menawarkan alternatif deteksi untuk PD. Sinyal EMG umumnya sulit dimengerti secara klinis. Penelitian ini menggunakan metode pengenalan pola sebagai alat untuk analisa sinyal EMG bagi subjek sehat dan subjek dengan PD. Sinyal EMG direkam dari 15 pasien dengan PD dan 8 subjek sehatdengan usia yang relatif muda saat subjek melakukan langkah jalan. Pengambilan data langkah jalan dilakukan di Rumah Sakit dr Kariadi Semarang, Jawa Tengah Indonesia. Dua belas buah fitur EMG digunakan untuk feature calculation pada sinyal EMG, delapan fitur adalah fitur yang berdasarkan domain waktu dan empat fitur berdasarkan domain frekuensi. Penelitian ini bertujuan untuk mengklasifikasikan dua kelas yaitu kelas untuk subjek sehat dan kelas untuk subjek PD menggunakan metode klasifikasi Artificial Neural Network (ANN) dengan jaringan feed forward dua layer. Jaringan menggunakan metode fungsi transfer log-sigmoid untuk hidden layer dan transfer fungsi softmax untuk output layer. ANN menggunakan 20 neuron pada hidden layer dan 2 neuron pada output layer. Metode training ANN menggunakan algoritma Levenberg-Marquardt dan penentuan nilai eror akurasi menggunakan metode mean square error (MSE). Berdasarkan hasil penghitungan, akurasi klasifikasi dua kelas untuk membedakan anatar subjek sehat dengan subjek PD sebesar 88.4%. Kata kunci: Artificial Neural Network (ANN), electromyography (EMG), gait test, Parkinson’s Disease

An online condition monitoring system implemented an internet connectivity and FTP for low speed slew bearing

Rapid and reliable information in slew bearing maintenance is not trivial issue. This paper presents the online monitoring system to assist maintenance engineer in order to monitor the bearing condition of low speed slew bearing in sheet metal company. The system is able to pass the vibration information from the place where the bearing and accelerometer sensors are attached to the data center; and from the data center it can be access by opening the online monitoring website from any place and by any person. The online monitoring system is built using some programming languages such as C language, MATLAB, PHP, HTML and CSS. Generally, the flow process is start with the automatic vibration data acquisition; then features are calculated from the acquired vibration data. These features are then sent to the data center; and form the data center, the vibration features can be seen through the online monitoring website. This online monitoring system has been successfully applied in School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong.Published versio