Abstract: According to a transformer-architecture-based method and system for IoT for and smart control of urban integrated energy, urban integrated energy IoT information acquired by a terminal from different areas is output to a server after being standardized into a sequence signal, where energy demand prediction is performed through learning of a deep learning network configured on the server, a smart control network is configured and built by using the deep learning network, real-time prediction processing is performed on the urban integrated energy IoT information acquired in real time, and a result is uploaded to a workstation for reviewing. Based on the transformer architecture, the deep learning network is combined with a fast Fourier transform algorithm and inverse transform on a sequence, and an FFT-Attention mechanism is proposed. Compared with a conventional transformer architecture, frequency domain information of a sequence is given more emphasis.

Abstract: A multi-user scheduling method based on reinforcement learning for 5G IoT, including: calculating the achievable rate of each user in a set according to a communication scenario model; generating an initial scheduled users set according to the achievable rate of each user; according to the achievable rate of each user and the number of times each user is scheduled, evaluating the estimated value of each user's action value under the current scheduling period by using the Q-learning method; determining the upper bound value of the confidence interval of each user's action value; determining the set of scheduled users under the current scheduling period according to the upper bound of the confidence interval of each user's action value; according to the set of scheduled users under the current scheduling period, recalculating the actual achievable rate value of each selected user under the current scheduling period.

Abstract: An SVM-based secure access method for wireless terminals. Step 1, establish a wireless network security model based on the communication scenarios of the wireless terminal and the wireless base station; establish the channel feature vectors based on the wireless network security model and the frames received by the wireless base station; Step 2, use the pre-trained SVM model to examine the channel feature vectors of the frames, determining whether the frames are legitimate signals; Step 3, determine whether the wireless terminal with at least one frame sent is a legitimate terminal based on whether at least one of the frames sent is a legitimate signal. The frames sent by the wireless terminals that access the wireless base station are examined to effectively determine legitimate terminals by improving the SVM parameter combination optimization method of differential evolution and the differential evolution method with an adaptive improvement of the control parameters.

Abstract: Disclosed is a method for calculating radio interference suffered by a communication device mounted on an electric power tower. The method includes: establishing a relative position model of the communication device mounted on the electric power tower and transmission lines, calculating interference field strength of the communication device on the electric power tower, and determining whether the radio interference field strength at the mounting point satisfies the requirements.

Abstract: An SVM-based secure access method for wireless terminals. Step 1, establish a wireless network security model based on the communication scenarios of the wireless terminal and the wireless base station; establish the channel feature vectors based on the wireless network security model and the frames received by the wireless base station; Step 2, use the pre-trained SVM model to examine the channel feature vectors of the frames, determining whether the frames are legitimate signals; Step 3, determine whether the wireless terminal with at least one frame sent is a legitimate terminal based on whether at least one of the frames sent is a legitimate signal. The frames sent by the wireless terminals that access the wireless base station are examined to effectively determine legitimate terminals by improving the SVM parameter combination optimization method of differential evolution and the differential evolution method with an adaptive improvement of the control parameters.

Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the L1 IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the Ll IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the L1 IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: A form filling application is configured to minimize the form filling effort of a user. The configuration follows concepts from reinforcement learning, and includes optimizing a policy for selecting agent actions in a manner that maximizes a reward signal. In the context of the form filling application, an agent action may specify one or more slots of the form for the user to fill, and further specify one or more user interfaces for filling the specified one or more slots. The reward signal may be defined as an inverse function of the user effort, so that maximizing the reward signal has the desired effect of minimizing the user effort.

Abstract: Disclosed is a method for calculating radio interference suffered by a communication device mounted on an electric power tower. The method includes: establishing a relative position model of the communication device mounted on the electric power tower and transmission lines, calculating interference field strength of the communication device on the electric power tower, and determining whether the radio interference field strength at the mounting point satisfies the requirements.

Abstract: An automated agent may communicate with a user via a chat channel to proactively alert the user of an L1 IT support issue. The L1 IT support issue may be determined based on monitoring indications of human-initiated activities maintained by a system of record, and may, prior to the automated agent's alert, be unknown to the user. In some instances, a natural language understanding (NLU) module may be used to identify an entity and intent from the indications of human-initiated activities, and the L1 IT support issue may be determined based on the determined entity and intent. After alerting the user of the L1 IT support issue, the automated agent may inform, via the chat channel, the user of a remediation step available to address the L1 IT support issue. Upon obtaining the user's permission, the automated agent may perform the remediation step to address the L1 IT support issue.

Abstract: A form filling application is configured to minimize the form filling effort of a user. The configuration follows concepts from reinforcement learning, and includes optimizing a policy for selecting agent actions in a manner that maximizes a reward signal. In the context of the form filling application, an agent action may specify one or more slots of the form for the user to fill, and further specify one or more user interfaces for filling the specified one or more slots. The reward signal may be defined as an inverse function of the user effort, so that maximizing the reward signal has the desired effect of minimizing the user effort.