4 research outputs found

    A low-cost closed-loop solar tracking system based on the sun position algorithm

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    Sun position and the optimum inclination of a solar panel to the sun vary over time throughout the day. A simple but accurate solar position measurement system is essential for maximizing the output power from a solar panel in order to increase the panel efficiency while minimizing the system cost. Solar position can be measured either by a sensor (active/passive) or through the sun position monitoring algorithm. Sensor-based sun position measuring systems fail to measure the solar position in a cloudy or intermittent day, and they require precise installation and periodic calibrations. In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. A standalone low-cost but high-precision dual-axis closed-loop sun-tracking system using the sun position algorithm was implemented in an 8-bit microcontroller platform. The Astronomical Almanac's (AA) algorithm was used for its simplicity, reliability, and fast computation capability of the solar position. Results revealed that incorporation of the sun position algorithm into a solar tracking system helps in outperforming the fixed system and optical tracking system by 13.9% and 2.1%, respectively. In summary, even for a small-scale solar tracking system, the algorithm-based closed-loop dual-axis tracking system can increase overall system efficiency. - 2019 Muhammad E. H. Chowdhury et al.The publication of this article was funded by the Qatar National Library. The authors would like to thank Qatar University for granting the student grant (QUST--CENG-SPR\2017-23) which made this work possible. We would like to thank the Mechanical Engineering Department, Qatar University, for their assistance in designing the mechanical system.Scopu

    Label self-advised support vector machine (LSA-SVM)-automated classification of foot drop rehabilitation case study

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    © 2019 Veterinary World. All rights reserved. Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures

    Identification of the rumination in cattle using support vector machines with motion-sensitive bolus sensors

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    The reticuloruminal function is central to the digestive efficiency in ruminants. For cattle, collar- and ear tag-based accelerometer monitors have been developed to assess the time spent ruminating on an individual animal. Cattle that are ill feed less and so ruminate less, thus, the estimation of the time spent ruminating provides insights into the health of individual animals. pH boluses directly provide information on the reticuloruminal function within the rumen and extended (three hours or more) periods during which the ruminal pH value remains below 5.6 is an indicator that dysfunction and poor welfare are likely. Accelerometers, incorporated into the pH boluses, have been used to indicate changes in behaviour patterns (high/low activity), utilised to detect the onset of oestrus. The paper demonstrates for the first time that by processing the reticuloruminal motion, it is possible to recover rumination periods. Reticuloruminal motion energy and the time between reticuloruminal contractions are used as inputs to a Support Vector Machine (SVM) to identify rumination periods with an overall accuracy of 86.1%, corroborated by neck mounted rumination collars
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