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To promote intelligent water services and accelerate the water industry's modernization process, accurately predicting regional residents' water demand and reducing energy consumption for secondary water supply is a major challenge for scientific scheduling and efficient management of urban water supply. This paper proposes a deep learning-based approach for demand forecasting in residential communities. The approach first identifies and corrects outliers in raw water supply data, and incorporates additional features such as epidemics and meteorological information.

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Citation contexts of a reference have many extensive applications in the area of computational linguistics and information retrieval. To use the citation contexts, it is common practice to employ the complete sentences in which the reference appears or the text in fixed window around the reference. Most of the time, the whole citation context does not particulary speaks about the target reference but only there is a sentence fragment which is the cause of making a reference.

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This is our experimental interface and environment. the algorithm's performance was evaluated using the UTIAS multi-robot CL and mapping dataset provided by Leung et al.

Each robot was equipped with a wheel encoder and a monocular camera, measuring linear and rotational velocities at 67 Hz and capturing distance and orientation measurements with other robots and landmarks. The position and orientation were obtained from a 10-camera Vicon motion capture system at 100 Hz, with a positional accuracy of approximately 1 mm.

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Coordinates in the Standard *.dat Format:

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The dataset contains detailed information on a 100kVA transformer in ONAN cooling mode. The file consists of three PDF documents, which describe the transformer’s overall structure, core structure and size, winding structure and dimension, respectively. 

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The RAHG experimental data includes six public datasets and one self-built dataset. The experimental process of RAHG on these seven datasets is also recorded in it

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The RAHG experimental data includes four public datasets and one self-built dataset. The experimental process of RAHG on these five datasets is also recorded.

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  This dataset is comprised of two parts. In dataset 1, we provide Buddha images, along with positions of hands and faces. Dataset 2 provides Buddha images only and can be used for Buddha statue classification.

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455 Views

Automatic generation control (AGC) of power generation units aims at providing satisfactory responses of generated active powers to desired active powers dispatched from a power grid center. This paper proposes a method to estimate two frequently-used AGC performance metrics of response rapidity and accuracy. The proposed method is composed of two main  parts . The first part selects step-like data segments as those being similar to a designed step-change time sequence, based on consecutive piece-wise linear representations of the desired active power.

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This dataset contains the raw data of the measurements/simulations presented in "Modulation Scheme Analysis for Low-Power Leadless Pacemaker Synchronization Based on Conductive Intracardiac Communication" by A. Ryser et al. This work analyzed the bit error rate (BER) performance of a prototype dual-chamber leadless pacemaker both in simulation and in-vitro experiments on porcine hearts.

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