Hybrid switch reluctance motors are the family of switch reluctance motors (SRMs) that attenuate the magnetic saturation and increase the air gap magnetic flux by exploiting permanent magnets. The permanent magnet aux...
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This article proposes a multimodal sentiment analysis system for recognizing a person’s aggressiveness in pain. The implementation has been divided into five components. The first three steps are related to a text-ba...
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We propose a cross-subcarrier precoder design(CSPD) for massive multiple-input multiple-output(MIMO) orthogonal frequency division multiplexing(OFDM) downlink. This work aims to significantly improve the channel estim...
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We propose a cross-subcarrier precoder design(CSPD) for massive multiple-input multiple-output(MIMO) orthogonal frequency division multiplexing(OFDM) downlink. This work aims to significantly improve the channel estimation and signal detection performance by enhancing the smoothness of the frequency domain effective channel. This is accomplished by designing a few vectors known as the transform domain precoding vectors(TDPVs), which are then transformed into the frequency domain to generate the precoders for a set of subcarriers. To combat the effect of channel aging, the TDPVs are optimized under imperfect channel state information(CSI). The optimal precoder structure is derived by maximizing an upper bound of the ergodic weighted sum-rate(WSR) and utilizing the a posteriori beam-based statistical channel model(BSCM). To avoid the large-dimensional matrix inversion, we propose an algorithm with symplectic optimization. Simulation results indicate that the proposed cross-subcarrier precoder design significantly outperforms conventional methods.
作者：
Jyothsna, D.Chandra, G. Ramesh
Department of Computer Science and Engineering Jntuh Hyderabad India
Department of Computer Science and Engineering Hyderabad India
In recent years, LIDARs (Light Detection and Ranging) have gained a lot of insight into various fields such as agriculture, astronomy, robotics, autonomous driving, forestry, etc. Point Clouds obtained through LIDAR h...
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Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, ...
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With the recent development of agriculture, the growing area and utilization rate of facilities are increasing, but it is necessary to control and prevent pests, and if the disease is detected at an early stage, appro...
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With the recent development of agriculture, the growing area and utilization rate of facilities are increasing, but it is necessary to control and prevent pests, and if the disease is detected at an early stage, appropriate treatment is possible. To this end, researches on control systems using artificial intelligence are being expanded recently, therefore we propose a pest diagnosis system using data acquisition and deep learning through collective intelligence. This study modeled the diagnostic system based on deep learning using the collective intelligence that the user group participates in the prediction of pests arising from the plant cultivation and the data registered by experts in the field. Diagnostic data were collected information on pest diagnosis registered on the Internet and used;the collected data were constructed as a data set that is easy to analyze, through preprocessing, types of crops were classified, pests data were studied through TensorFlow. Most of the researches for the control and prevention of pests are based on web-based expert system. In this paper, we collect data through the collective intelligence and the general public. Especially, when a user uses input question and answers data without a formalized format, it gives wrong prediction;therefore, the preprocessing process was performed for data analysis because it could adversely affect the reliability of the system. After the data collection and preprocessing process is completed, a prediction model is created using TensorFlow, an artificial intelligence open source framework, using the generated data set. The user was allowed to input arbitrary data values while testing the data one to five times based on the data value and the effective value of the prediction model was confirmed according to the change of the value. Through the research, it is proved that diagnosis of pests is possible by using collective intelligence. In future research, research on the construction of a system
Blockchain is a magnificent technology that could decentralize data and information management, distribution, and storage. With the implementation of blockchain technology, Current corporate operations should be impro...
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This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks ...
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The escalating installation of distributed generation (DG) within active distribution networks (ADNs) diminishes the reliance on fossil fuels, yet it intensifies the disparity between demand and generation across vari...
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The escalating installation of distributed generation (DG) within active distribution networks (ADNs) diminishes the reliance on fossil fuels, yet it intensifies the disparity between demand and generation across various regions. Moreover, due to the intermittent and stochastic characteristics, DG also introduces uncertain forecasting errors, which further increase difficulties for power dispatch. To overcome these challenges, an emerging flexible interconnection device, soft open point (SOP), is introduced. A distributionally robust chance-constrained optimization (DRCCO) model is also proposed to effectively exploit the benefits of SOPs in ADNs under uncertainties. Compared with conventional robust, stochastic and chance-constrained models, the DRCCO model can better balance reliability and economic profits without the exact distribution of uncertainties. More-over, unlike most published works that employ two individual chance constraints to approximate the upper and lower bound constraints (e.g, bus voltage and branch current limitations), joint two-sided chance constraints are introduced and exactly reformulated into conic forms to avoid redundant conservativeness. Based on numerical experiments, we validate that SOPs' employment can significantly enhance the energy efficiency of ADNs by alleviating DG curtailment and load shedding problems. Simulation results also confirm that the proposed joint two-sided DRCCO method can achieve good balance between economic efficiency and reliability while reducing the conservativeness of conventional DRCCO methods.
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