Abstract: An electronic equipment accessory includes an accessory body and a holder assembly connected to the accessory body. The holder assembly includes a support member and a base. The base is rotatably connected to the accessory body. The support member is rotatable relative to the base between an extended position and a folded position. At the extended position, an included angle is formed between the support member and the base to thereby allow the accessory body to be supported on a support surface. The support member is rotatable relative to the base in one direction and the base is rotatable relative to the accessory body in another direction, thus the support member of the electronic equipment accessory can be adjusted with respect to the accessory body in multiple directions to thereby allow the electronic equipment accessory to support electronic equipment on a support surface at multiple different directions or tilted angles.

Abstract: An electronic equipment accessory includes an accessory body and a holder assembly connected to the accessory body. The holder assembly includes a support member and a base. The base is rotatably connected to the accessory body. The support member is rotatable relative to the base between an extended position and a folded position. At the extended position, an included angle is formed between the support member and the base to thereby allow the accessory body to be supported on a support surface. The support member is rotatable relative to the base in one direction and the base is rotatable relative to the accessory body in another direction, thus the support member of the electronic equipment accessory can be adjusted with respect to the accessory body in multiple directions to thereby allow the electronic equipment accessory to support electronic equipment on a support surface at multiple different directions or tilted angles.

Abstract: An integrated detection test device for a drill bit and a rotary blowout preventer and a detection method thereof are provided. The test device includes a support frame, a rotary shaft is mounted on the support frame, and an upper end of the rotary shaft is connected to a piston rod of a first hydraulic cylinder fixed on the support frame; and the rotary shaft is connected to a driving device through a rotary disc sleeved outside the rotary shaft. A pit sinking to a fixing plane of the support frame is arranged below the support frame; and an L-shaped track and a transport vehicle slidably connected to the track are mounted in the pit and used to transport the rotary blowout preventer or the drill bit test bench to a test position.

Abstract: A multifunctional rotary blowout preventer test device and a method thereof are provided. The device includes a test box body and a rotary blowout preventer body, a base is fixedly connected inside the test box body with screws, and two first receiving blocks are fixedly connected to a top of the base with screws. Through the arrangement of the structures such as a motor and an output shaft, the motor is started, the output shaft is driven to rotate, a rotating rod is driven to rotate, a forward-rotating screw rod and a reverse-rotating screw rod are driven to rotate, a right-moving sliding block and a left-moving sliding block are driven to move.

Abstract: The present invention discloses a choke combination device for gas production. wherein a first boss is arranged on an inner wall of the choke sleeve, a first through hole is formed in the middle of the first boss, a second boss is arranged on an outer peripheral side surface of the choke; the plug is provided with a stepped hole; outer walls of the first cylinder and the second cylinder are in sliding connection with a key groove in the inner wall of the choke sleeve, inner walls of the first cylinder and the second cylinder are in threaded connection with the first shaft section and the second shaft section respectively. The device is simple to replace and operate, which can save the operating time of replacement operation.

Abstract: A server computing device, including memory storing a knowledge graph. The server computing device may further include a processor configured to receive a natural language input and generate a tokenized utterance based on the natural language input. Based on the tokenized utterance, the processor may select a predefined intention indicating a target ontology entity type of the natural language input. The processor may identify at least one input ontology entity token included in the tokenized utterance and may identify at least one relation between the predefined intention and the input ontology entity token. Based on the predefined intention, the at least one input ontology entity token, and the relation, the processor may generate a structured query. Based on the structured query and the knowledge graph, the processor may output an output ontology entity token having the target ontology entity type.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities connected by a plurality of edges. The server computing device may further include a processor configured to generate a glossary file based on the knowledge graph. The glossary file may include a plurality of ontology entities included in the knowledge graph. The processor may receive a plurality of utterance templates. Each utterance template may include an utterance and a predefined intention. For each utterance template, the processor may generate one or more utterance template copies in which one or more ontology entities included in the utterance are replaced with one or more utterance template fields. The processor may generate a plurality of training utterances at least in part by filling the one or more utterance template fields of the one or more utterance template copies with respective ontology entities included in the glossary file.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities. The server computing device may further include a processor configured to receive a tokenized utterance including a plurality of words and one or more metadata tokens. The processor may extract a respective word embedding vector from each word included in the tokenized utterance. Based on a glossary file, the processor may determine a respective ontology entity type of each word included in the tokenized utterance. The processor may extract a character embedding vector from each character included in the tokenized utterance. Based on the plurality of word embedding vectors, the plurality of respective ontology entity types of the words, and the plurality of character embedding vectors, the processor may determine a predefined intention of the tokenized utterance using at least one recurrent neural network. The predefined intention may indicate a target ontology entity type.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities connected by a plurality of edges. The server computing device may further include a processor configured to generate a glossary file based on the knowledge graph. The glossary file may include a plurality of ontology entities included in the knowledge graph. The processor may receive a plurality of utterance templates. Each utterance template may include an utterance and a predefined intention. For each utterance template, the processor may generate one or more utterance template copies in which one or more ontology entities included in the utterance are replaced with one or more utterance template fields. The processor may generate a plurality of training utterances at least in part by filling the one or more utterance template fields of the one or more utterance template copies with respective ontology entities included in the glossary file.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities. The server computing device may further include a processor configured to receive a tokenized utterance including a plurality of words and one or more metadata tokens. The processor may extract a respective word embedding vector from each word included in the tokenized utterance. Based on a glossary file, the processor may determine a respective ontology entity type of each word included in the tokenized utterance. The processor may extract a character embedding vector from each character included in the tokenized utterance. Based on the plurality of word embedding vectors, the plurality of respective ontology entity types of the words, and the plurality of character embedding vectors, the processor may determine a predefined intention of the tokenized utterance using at least one recurrent neural network. The predefined intention may indicate a target ontology entity type.

Abstract: A server computing device, including memory storing a knowledge graph. The server computing device may further include a processor configured to receive a natural language input and generate a tokenized utterance based on the natural language input. Based on the tokenized utterance, the processor may select a predefined intention indicating a target ontology entity type of the natural language input. The processor may identify at least one input ontology entity token included in the tokenized utterance and may identify at least one relation between the predefined intention and the input ontology entity token. Based on the predefined intention, the at least one input ontology entity token, and the relation, the processor may generate a structured query. Based on the structured query and the knowledge graph, the processor may output an output ontology entity token having the target ontology entity type.

Abstract: A system and method for speech translation includes a bridge module connected between a first component and a second component. The bridge module includes a transformation model configured to receive an original hypothesis output from a first component. The transformation model has one or more transformation features configured to transform the original hypothesis into a new hypothesis that is more easily translated by the second component.

Abstract: A system and method for training a statistical machine translation model and decoding or translating using the same is disclosed. A source word versus target word co-occurrence matrix is created to define word pairs. Dimensionality of the matrix may be reduced. Word pairs are mapped as vectors into continuous space where the word pairs are vectors of continuous real numbers and not discrete entities in the continuous space. A machine translation parametric model is trained using an acoustic model training method based on word pair vectors in the continuous space.

Abstract: A system and method for speech translation includes a bridge module connected between a first component and a second component. The bridge module includes a transformation model configured to receive an original hypothesis output from a first component. The transformation model has one or more transformation features configured to transform the original hypothesis into a new hypothesis that is more easily translated by the second component.

Abstract: A system and method for training a statistical machine translation model and decoding or translating using the same is disclosed. A source word versus target word co-occurrence matrix is created to define word pairs. Dimensionality of the matrix may be reduced. Word pairs are mapped as vectors into continuous space where the word pairs are vectors of continuous real numbers and not discrete entities in the continuous space. A machine translation parametric model is trained using an acoustic model training method based on word pair vectors in the continuous space.

Abstract: A method of modeling a speech recognition system includes decoding a speech signal produced from a training text to produce a sequence of predicted speech units. The training text comprises a sequence of actual speech units that is used with the sequence of predicted speech units to form a confusion model. In further embodiments, the confusion model is used to decode a text to identify an error rate that would be expected if the speech recognition system decoded speech based on the text.