Data Acquisition Section Research Articles

EMG 데이터 취득 구간별 딥러닝을 이용한 실시간 손동작 예측

EMG 데이터 취득 구간별 딥러닝을 이용한 실시간 손동작 예측

Progress of the control system for negative ion source based neutral beam injector prototype at ASIPP

The negative ion source based neutral beam injector (NNBI) will play an important role in Comprehensive Research fAcility for Fusion Technology (CRAFT). In order to explore the physics and engineering issues for NNBI, the RF negative ion based test facility has been tested with cesium seeding, negative ion production, and extraction at Institute of Plasma Physics, Chinese Academy of Sciences (ASIPP). According to the architecture of ITER CODAC (Control, Data Access and Communication) system and the experimental requirements of negative ion test facility, the control system for negative ion source based neutral beam injector prototype has been developed. It fulfills the physics experimental requirements, and what's more, it accumulates design experience for control system of CRAFT NNBI. In this paper, the control and data acquisition system are described, especially the real-time control, plant units interface and central control program. In the data acquisition section, the requirements are presented not only for the system, but also an improvement plan is given. Meanwhile, the interlock system is also present, including system architecture design and classification of interlock signals.

Design and Implementation of Artificial Neural Networks to Predict Wind Directions on Controlling Yaw of Wind Turbine Prototype

Wind energy as one of the new renewable energies has an important role in replacing fossil energy sources in Indonesia. In order to make the wind turbine's performance more efficient in extracting energy from the wind, it is necessary to control the actuation movements pitch and yaw of the wind turbine horizontal . Controlling the actuator yaw can increase the absorption efficiency of the power to the rotor face toward the direction of the wind. The purpose of this thesis is to be able to predict the direction of the coming wind, then move the turbine rotor in the predicted direction. In this final project a wind turbine prototype is used with a precision of 5.3%, then for the data acquisition section, a wind direction sensor is built to change the amount of wind direction to a quantity that can be measured in units of degrees, and anemometer to measure wind speed. In making the wind direction prediction algorithm, artificial neural network (ANN) method is used with input parameters such as wind speed, temperature, humidity, pressure, and altitude. Data acquisition is done at one minute intervals with long data collection for one day, 1072 data are obtained, the data is then fed to the ANN model that has been prepared. Based on the results of tests that have been done, it is found that the Mean Absolute Error in the model is 0.4%.

Classification via an Embedded Approach

This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an array of five tin dioxide (SnO2) gas sensors, an Arduino Uno board used as a data acquisition section, as well as an intelligent classification module by embedding an approach function which receives data signals from the electronic nose. For the intelligent classification module, a training algorithm is also implemented to create the base of a portable, automated, fast-response, and economical electronic nose device. This solution proposes a portable system to identify and classify VOCs without using a personal computer (PC). Results show an acceptable precision for the embedded approach in comparison with the performance of a toolbox used in a PC. This constitutes an embedded solution able to recognize VOCs in a reliable way to create application products for a wide variety of industries, which are able to classify data acquired by an electronic nose, as VOCs. With this proposed and implemented algorithm, a precision of 99% for classification was achieved into the embedded solution.

Sensor Based Human Movement Controlled Hydraulic and Electrical Robotic Arm

This system is designed for advance Robotic control. It based on sensor data acquisition and software data processing. With those systems controlling a robotic hand by hydraulic and electric means. It is separated by two different sections. First, data acquisition section with differential sensor data (Gyro sensor, Flex sensor, Pressure sensor). Second, software processed data application system consisting of robotic hand. Specialty of this system is it gives precise control of robotic arm following human hand movement. It also gives touch and pressure feelings in robotic hand. A lot of work can be done easily with the help of it. Like this system gives remote bomb disposal, hazardous environmental work remotely, remote operation, remote medical help and so on.

A fuzzy inference system for the identification

Odor identification is an important area in a wide range of industries like cosmetics, food, beverages and medical diagnosis among others. Odor detection could be done through an array of gas sensors conformed as an electronic nose where a data acquisition module converts sensor signals to a standard output to be analyzed. To facilitate odors detection a system is required for the identification. This paper presents the results of an automated odor identification process implemented by a fuzzy system and an electronic nose. First, an electronic nose prototype is manufactured to detect organic compounds vapor using an array of five tin dioxide gas sensors, an arduino uno board is used as a data acquisition section. Second, an intelligent module with a fuzzy system is considered for the identification of the signals received by the electronic nose. This solution proposes a system to identify odors by using a personal computer. Results show an acceptable precision.

High-Speed Real-Time Ion Detection System for Time-of-Flight Mass Spectrometry Application

ABSTRACT This paper presents a high-speed ion detection system for time-of-flight mass spectroscopy (TOFMS) applications. The system consists of the data acquisition, distribution, and storage section. The output signal from a microchannel plate (MCP) of a mass analyzer will be firstly digitized by a 3 Gsps high-speed analog-to-digital converter (ADC) in the acquisition section. Then the digitized data stream will be distributed into four streams and be stored into four solid-state storage cards. The read-back data can be restored for further processing. System evaluation results show that the system has wide input bandwidth, high sample rate (up to 3.0 GB/s), good signal-to-noise ratio (SNR; up to 45 dB), and high data throughput (over 3 GB/s). That makes the system suitable for improving dynamic detection performance in TOFMS applications.

Designing Experiment Platform for Micro Heat Exchangers

Micro heat exchanger, with its advantages such as small volume, light weight, high efficiency etc. has an increasing demand of market needs and applications. To evaluate the performance of a particular micro heat exchanger, this paper provides a novel platform for testing and inspection. Four sections are involved including the working principle of the experiment platform, component parts and arrangement, experiment and the data analysis. As for the hardware, precision instruments such as pressure gauge, flow meter and temperature sensors are utilized in the experiment in the data acquisition section. While for the software aspect, data acquisition and processing system based on the Visual Basic is applied in this experiment. The effect of the platform for micro heat exchanger is supported by the results of the testing .

EANM Dosimetry Committee guidelines for bone marrow and whole-body dosimetry

The level of administered activity in radionuclide therapy is often limited by haematological toxicity resulting from the absorbed dose delivered to the bone marrow. The purpose of these EANM guidelines is to provide advice to scientists and clinicians on data acquisition and data analysis related to bone-marrow and whole-body dosimetry. The guidelines are divided into sections "Data acquisition" and "Data analysis". The Data acquisition section provides advice on the measurements required for accurate dosimetry including blood samples, quantitative imaging and/or whole-body measurements with a single probe. Issues specific to given radiopharmaceuticals are considered. The Data analysis section provides advice on the calculation of absorbed doses to the whole body and the bone marrow. The total absorbed dose to the bone marrow consists of contributions from activity in the bone marrow itself (self-absorbed dose) and the cross-absorbed dose to the bone marrow from activity in bone, larger organs and the remainder of the body. As radionuclide therapy enters an era where patient-specific dosimetry is used to guide treatments, accurate bone-marrow and whole-body dosimetry will become an essential element of treatment planning. We hope that these guidelines will provide a basis for the optimization and standardization of the treatment of cancer with radiopharmaceuticals, which will facilitate single- and multi-centre radionuclide therapy studies.

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