Voice recognition and command technology for applications with industrial robots is a relatively new field in the intelligent manufacturing industry. It offers a number of advantages over other methods of communicatio...
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ISBN:
(纸本)9781665489218
Voice recognition and command technology for applications with industrial robots is a relatively new field in the intelligent manufacturing industry. It offers a number of advantages over other methods of communication with robots, as it requires fewer specialized skills to manipulate the robot workstation. Additionally, using voice commands can help reduce the number of industrial injuries caused by contact with machinery, thus potentially save operators' lives in emergency situations where external assistance is not immediately available. This study presents a design of a Cartesian robot workstation which is equipped with a voice recognition system controlled by audio commands, as well as a vision perception system. The vision perception system uses the Real Sense depth camera that captures information about the coordinates of the work pieces, which is processed by SSD algorithm. The voice recognition system has been developed with an algorithm which combines both lstm and HMM, and it has good performance in term of both efficiency and accuracy in controlling normal operation as well as emergency stop for our robot grasping workstation.
Fuzzy logic, machine learning, and artificial intelligence (AI) together provide a ground-breaking approach to increased security. This change in perspective creates new opportunities that may aid in identifying vulne...
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The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-lstm combined forecasting model...
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The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-lstm combined forecasting model based on the autoregressive integrated moving average (ARIMA) model and the long short-term memory (lstm) recurrent neural network. In the ARIMA-lstm model, the ARIMA model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The lstm model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R-2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined ARIMA-lstm model are R-2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has the highest prediction accuracy and the lowest error and is more suitable for the predictive analysis of gas data. By comparing the prediction results of a single model and the combined model on gas time series data, the applicability, validity and scientificity of the combined model proposed in this paper are verified, which is of great importance to accurate prediction and early warning of underground gas danger in coal mines.
Budget, as an important component of management accounting, is an effective means for companies to achieve functions such as planning, coordination, and control. It is a bridge and link connecting different units and ...
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Budget, as an important component of management accounting, is an effective means for companies to achieve functions such as planning, coordination, and control. It is a bridge and link connecting different units and departments within the company and economic operations. However, current budget management pays less attention to temporal characteristics, leading to budget ambiguity. Taking Company A as an example, the long short-term memory (lstm) algorithm was used to collect and process historical data and predict its future budget and revenue situation. It was found that the budget management of Company A was relatively chaotic, with insufficient investor information, and the predicted results were close to the actual situation, proving the effectiveness of the model proposed in this paper.
This paper presents a study on the possibility of predicting the regional ionosphere at midlatitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the Sami2 is Another Model...
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This paper presents a study on the possibility of predicting the regional ionosphere at midlatitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the Sami2 is Another Model of the Ionosphere (SAMI2). The NN model was constructed from the data set of Jeju ionosonde (33.43 degrees N, 126.30 degrees E) for the period of 1 January 2011 to 31 December 2015 by using the long-short term memory (lstm) algorithm. The NN model provides 24-hr prediction of the peak density (NmF2) and peak height (hmF2) of the F2 layer over Jeju. The predicted NmF2 and hmF2 were used to compute two ionospheric drivers (total ion density and effective neutral meridional wind), which were assimilated into the SAMI2 model. The SAMI2-lstm model estimates the ionospheric conditions over the midlatitude region around Jeju on the same geomagnetic meridional plane. We evaluate the performance of the SAMI2-lstm by comparing predicted NmF2 and hmF2 values with measured values during the geomagnetic quiet and storm periods. The root-mean-square error values of NmF2 (hmF2) from Jeju ionosonde measurements are lower by 45% and 45% (30% and 11%) than those of the SAMI2 and IRI-2016 models during the geomagnetic quiet periods. However, during the geomagnetic storm periods, the performance of the SAMI2-lstm model does not predict positive geomagnetic storms well. Comparing the quiet and storm periods for the SAMI2-lstm model, the root-mean-square error (RMSE) of the storm period was calculated to be 2.76 (3.2) times higher at Jeju (Icheon) than in the quiet period. From these results, we demonstrated that in this study, the combination of the NN-lstm model and physics-based model could improve the ionosphere prediction of existing theoretical and empirical models for midlatitude regions, at least in geomagnetically quiet conditions. We strongly suggest that this attempt, which has not been reported before, could be used as one of the keys to advance the physics-base
The research background of military communication network starts from the early period of human society. Affected by the continuous development of science and technology, military communication plays an increasingly p...
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ISBN:
(纸本)9798400716775
The research background of military communication network starts from the early period of human society. Affected by the continuous development of science and technology, military communication plays an increasingly prominent role in war. In today's society, military communication network has serious error rate and low efficiency, which has great influence on military information transmission. In this paper, the military communication network system with K-means algorithm is adopted, and the conclusion is drawn through experiments. The algorithm greatly improves the accuracy and efficiency of military communication networks, increasing the accuracy rate to 99.6%.
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