Instrument Identification Technology Based on Deep Learning

With the continuous improvement of science and technology, the substation remote control system has been constantly improved, which provides the possibility for the complete realization of intelligent and unmanned substation. However, due to the special substation environment, it is easy to cause interference, coupled with the low accuracy of today’s video image processing algorithm, which leads to the frequent occurrence of false alarms and missing alarms. Manual intervention is needed to deal with this, which inhibits the display of automatic intelligent substation processing functions. Therefore, in this paper, the most rapidly developed machine learning algorithm — deep learning is applied to the substation instrument equipment identification processing, in order to improve the accuracy and efficiency of instrument equipment identification, and make due contributions to the full realization of unattended substation.

An Automatic Instrument Recognition Approach Based on Deep Convolutional Neural Network

Background: This paper presents an automatic instrument recognition method highlighting the deep learning aspect of instrument identification in order to advance the automatic process of video monitoring remotely equipment of substation. Methodology: This work utilizes the Scale Invariant Feature Transform approach (SIFT) and the Gaussian difference model for instrument positioning while proposing a design scheme of instrument identification system. Results: The experimental outcomes obtained proves that the proposed system is capable of automatically recognizing a modest graphical interface and study independently while improving the operation effectiveness of appliance and realizing the purpose of spontaneous self-check. The proposed approach is applicable for musical instrument recognition and it provides 92% of the accuracy rate, 87.5% precision value and recall rate of 91.2%. Conclusion: The comparative analysis with other state of the art methods justifies that the proposed deep learning based music recognition method outperforms the other existing approaches in terms of accuracy, thereby providing a practicable music instrument recognition solution.

Assessing the efficacy of tablet-based simulations for learning pseudo-surgical instrumentation

Nurses and surgeons must identify and handle specialized instruments with high temporal and spatial precision. It is crucial that they are trained effectively. Traditional training methods include supervised practices and text-based study, which may expose patients to undue risk during practice procedures and lack motor/haptic training respectively. Tablet-based simulations have been proposed to mediate some of these limitations. We implemented a learning task that simulates surgical instrumentation nomenclature encountered by novice perioperative nurses. Learning was assessed following training in three distinct conditions: tablet-based simulations, text-based study, and real-world practice. Immediately following a 30-minute training period, instrument identification was performed with comparable accuracy and response times following tablet-based versus text-based training, with both being inferior to real-world practice. Following a week without practice, response times were equivalent between real-world and tablet-based practice. While tablet-based training does not achieve equivalent results in instrument identification accuracy as real-world practice, more practice repetitions in simulated environments may help reduce performance decline. This project has established a technological framework to assess how we can implement simulated educational environments in a maximally beneficial manner.

Assessing the efficacy of tablet-based simulations for learning pseudo-surgical instrumentation

IntroductionNurses and surgeons must identify and handle specialized instruments with high temporal and spatial precision. It is crucial that they are trained effectively. Traditional methods, including supervised practices and text-based study, can expose patients to undo risk or neglect motor/haptic training respectively. Tablet-based simulations have been proposed to mediate some of these limitations.MethodsWe implement a learning task that simulates surgical instrumentation nomenclature encountered by novice perioperative nurses. Learning was assessed following training in three distinct conditions: tablet-based simulations, text-based study, and real-world practice.ResultsImmediately following a 30 minute training period, instrument identification was performed with comparable accuracy and response times following tablet-based versus text-based training, with both inferior to real-world practice. Following a week without practice, response times were equivalent between real-world and tablet-based practice.ConclusionsWhile tablet-based training does not achieve equivalent results in instrument identification accuracy as real-world practice, more practice repetitions in simulated environments may help reduce performance decline from delayed implementation of learning. This project has established a technological framework to assess how we can implement simulated educational environments in a maximally beneficial manner.

Various Approaches in Musical Instrument Identification

In the music information retrieval (MIR) field, it is highly desirable to know what instruments are used in an audio sample. Musical instrument classification is one of the sub domains of music information retrieval. Many researchers have presented different approaches for identifying western instruments and those approaches proved to be good for instrument identification. In this article, we have presented work done by the various authors to identify musical instrument using various approaches such sparse based representation, bio-inspired hierarchical model, joint modelling, Bayesian networks, neural networks, convolution neural networks, individual partials, clustering, and segmentation.