公开(公告)号：US20170023992A1

公开(公告)日：2017-01-26

申请号：US15100209

申请日：2014-11-21

Applicant: Tata Consultancy Services Limited

Inventor： UMESH SINGH ,  HARSHAD GIRISH BHAGWAT ,  SANKARA NARAYANAN D ,  ARUN VARGHESE ,  AMARENDRA KUMAR SINGH ,  RAJESH JAYAPRAKASH ,  ANAND SIVASUBRAMANIAM

IPC: G06F1/20

CPC classification number: G06F1/20 ,  G05B2219/50333 ,  G06F11/00 ,  G06F17/5009 ,  G06F2217/80

Abstract: Disclosed is a system and method for facilitating optimizing cooling efficiency of a data center. The method may comprise receiving a layout of the data center. The method may comprise computing co-ordinates of each equipment of a plurality of equipments associated with the data center. Further, the method may comprise segregating the layout into a plurality of cells. The method may comprise capturing preliminary data associated with the data center. Further, the method may comprise determining a state value of the data center based upon the preliminary data. The method may comprise capturing CFD data and, selectively, thermal assessment data. Further, the method may comprise facilitating the optimization of the cooling efficiency of the data center by using an external analysis tool capable of performing Computational Fluid Dynamics (CFD) analysis or thermal assessment followed by the Computational Fluid Dynamics (CFD) analysis using the CFD data and the thermal assessment data.

Abstract translation: 公开了一种有利于优化数据中心的冷却效率的系统和方法。 该方法可以包括接收数据中心的布局。 该方法可以包括与数据中心相关联的多个设备的每个设备的坐标。 此外，该方法可以包括将布局分离成多个单元。 该方法可以包括捕获与数据中心相关联的初始数据。 此外，该方法可以包括基于初步数据确定数据中心的状态值。 该方法可以包括捕获CFD数据和选择性地热评估数据。 此外，该方法可以包括通过使用能够执行计算流体力学（CFD）分析或热评估的外部分析工具来促进数据中心的冷却效率的优化，随后使用CFD数据进行计算流体力学（CFD）分析 和热评估数据。

公开(公告)号：US20230125620A1

公开(公告)日：2023-04-27

申请号：US17935420

申请日：2022-09-26

Applicant: Tata Consultancy Services Limited

Inventor： SAGAR KUMAR VERMA ,  SUPRIYA AGRAWAL ,  VENKATESH RAMANATHAN ,  ULKA SHROTRI ,  SRINARAYANA NAGARATHINAM ,  RAJESH JAYAPRAKASH ,  AABRITI DUTTA

IPC: F24F11/63

Abstract: HVAC control system's supervisory control is crucial for energy-efficient thermal comfort in buildings. The control logic is usually specified as ‘if-then-that-else’ rules that capture the domain expertise of HVAC operators, but they often have conflict that may lead to sub-optimal HVAC performance. Embodiments of the present disclosure provide a method and system for optimized Heating, ventilation, and air-conditioning (HVAC) control using domain knowledge combined with Deep Reinforcement Learning (DRL). The system disclosed utilizes Deep Reinforcement Learning (DRL) for conflict resolution in a HVAC control in combination with domain knowledge in form of control logic. The domain knowledge is predefined in an Expressive Decision Tables (EDT) engine via a formal requirement specifier consumable by the EDT engine to capture domain knowledge of a building for the HVAC control.

公开(公告)号：US20240112096A1

公开(公告)日：2024-04-04

申请号：US18455045

申请日：2023-08-24

Applicant: Tata Consultancy Services Limited

Inventor： ROHITH REGIKUMAR ,  PRIYANGA KASTHURIRAJAN ,  RAJESH JAYAPRAKASH ,  ARVIND RAMANUJAM

IPC: G06Q10/047 ,  G06N3/091

CPC classification number: G06Q10/047 ,  G06N3/091 ,  G06Q50/30

Abstract: The present disclosure provides a system and method for delay prediction for scheduled public transport. A multi-architectural deep learning approach has been used to predict the delays of a queried vehicle in the scheduled public transport. For this, historical operational data is transformed into temporal, and spatiotemporal data. While, the spatial data is obtained from geographical information. The system uses different combinations of neural networks architectures. A regressor model uses three separate kinds of architecture. One component is the Fully Connected Neural Network (FCNN), which is good at learning from static features, the second is the Long Short Term Memory (LSTM) network which is good at learning from temporal features, and the third is the 3D Convolutional Neural Network (3DCNN) which is good at learning from spatiotemporal features. Learned encoding from each are fed to another FCNN to produce the predicted delay value.

公开(公告)号：US20230401442A1

公开(公告)日：2023-12-14

申请号：US18318380

申请日：2023-05-16

Applicant: Tata Consultancy Services Limited

Inventor： KRISHNAN SATHEESH ,  ROHITH REGIKUMAR ,  ARVIND RAMANUJAM ,  RAJESH JAYAPRAKASH

IPC: G06N3/08 ,  G06Q50/30 ,  G06F16/9537 ,  G06Q10/04

CPC classification number: G06N3/08 ,  G06Q50/30 ,  G06F16/9537 ,  G06Q10/04

Abstract: The present disclosure predicts a delay associated with a vehicle. Conventional methods are mainly mathematical based and machine learning based networks are not predicting delay accurately. Initially, the present disclosure Initially, the system receives a user query comprising an expected delay of a target vehicle in at least one target station. Further, a real time data associated with the user query in a predefined horizon is obtained. Further, a spatial feature vector, a temporal feature vector and spatiotemporal features are extracted based on the real time data using a feature extraction technique. Finally, the expected is predicted based on the plurality of features using a trained adversarial regression model, wherein the trained adversarial regression model comprises a critic network and a regressor network. The regressor network is trained with a plurality of architectures and a best architecture with minimum Mean Absolute Error (MAE) is selected for delay prediction.