Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: An Internet-of-Things management and control system for an intelligent orchard includes a server, agricultural machinery equipment, an image acquisition apparatus disposed on the site, and various sensors. The agricultural machinery equipment, the image acquisition apparatus and the various sensors are in communication connection with the server. The server includes an orchard management subsystem and an information monitoring subsystem. The orchard management subsystem includes a fruit tree planting planning module, a task management module and various information management modules, and the information monitoring subsystem includes a meteorological environment monitoring module, a soil moisture monitoring module and a disease and pest monitoring module. According to the Internet-of-Things management and control system, all-round management for an orchard from planning to picking can be achieved.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: An Internet-of-Things management and control system for an intelligent orchard includes a server, agricultural machinery equipment, an image acquisition apparatus disposed on the site, and various sensors. The agricultural machinery equipment, the image acquisition apparatus and the various sensors are in communication connection with the server. The server includes an orchard management subsystem and an information monitoring subsystem. The orchard management subsystem includes a fruit tree planting planning module, a task management module and various information management modules, and the information monitoring subsystem includes a meteorological environment monitoring module, a soil moisture monitoring module and a disease and pest monitoring module. According to the Internet-of-Things management and control system, all-round management for an orchard from planning to picking can be achieved.

Abstract: A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

Abstract: A method for determining the preload value of the screw based on thermal error and temperature rise weighting. Firstly, thermal behavior test of the feed shaft under typical working conditions is carried out to obtain the maximum thermal error and the temperature rise at the key measuring points in each preloaded state. Then, a mathematical model of the preload value of the screw and the maximum thermal error is established; meanwhile, another mathematical model of the preload value of the screw and the temperature rise at the key measuring points is also established. Finally, the optimal preload value of the screw is obtained. The thermal error of the feed shaft and the temperature rise of the moving components are comprehensively considered, improving the processing accuracy and accuracy stability of the machine tool, and ensuring the service life of the moving components such as bearings.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

Abstract: An on line prediction method of part surface roughness based on SDAE-DBN algorithm. The tri-axis acceleration sensor is adsorbed on the rear bearing of the machine tool spindle through the magnetic seat to collect the vibration signals of the cutting process, and a microphone is placed in the left front of the processed part to collect the noise signals of the cutting process of the machine tool; the trend term of dynamic signal is eliminated, and the signal is smoothed; a stacked denoising autoencoder is constructed, and the greedy algorithm is used to train the network, and the extracted features are used as the input of deep belief network to train the network; the real-time vibration and noise signals in the machining process are input into the deep network after data processing, and the current surface roughness is set as output by the network.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: The invention provides a data augmentation method based on generative adversarial networks in tool condition monitoring. Firstly, the sensor acquisition system is used to obtain the vibration signal and noise signal during the cutting process of the tool; second, the noise data subject to the prior distribution is input to the generator to generate data, and the generated data and the collected real sample data are input to the discriminator for identification, the confrontation training between the generator and the discriminator until the training is completed; then, use the trained generator to generate sample data, and determine whether the generated sample data and the actual tool state sample data are similar in distribution; finally, combined with the accuracy of the deep learning network model to predict the state of the tool to verify the availability of the generated data.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: A method for determining the preload value of the screw based on thermal error and temperature rise weighting. Firstly, thermal behavior test of the feed shaft under typical working conditions is carried out to obtain the maximum thermal error and the temperature rise at the key measuring points in each preloaded state. Then, a mathematical model of the preload value of the screw and the maximum thermal error is established; meanwhile, another mathematical model of the preload value of the screw and the temperature rise at the key measuring points is also established. Finally, the optimal preload value of the screw is obtained. The thermal error of the feed shaft and the temperature rise of the moving components are comprehensively considered, improving the processing accuracy and accuracy stability of the machine tool, and ensuring the service life of the moving components such as bearings.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.