In this paper, we propose an equivalent transmit beamforming method in joint range and angle domains at the receiver of the colocated transmit-receive system where frequency diverse array (FDA) acts as transmit antenn...
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In this paper, we propose an equivalent transmit beamforming method in joint range and angle domains at the receiver of the colocated transmit-receive system where frequency diverse array (FDA) acts as transmit antenna. FDA employs a small frequency offset across the array elements and introduces additional degrees-of-freedom in range domain, which can significantly enhance the beamforming flexibility. However, the transmit beam pattern of conventional FDA is range-angle-time dependent. In this work, the time-varying problem is first solved by using a series of filters and mixers at the receiver. In the sequel, a subarray-based FDA framework, termed as multisub-FDA, is established and then a range-angle-decoupled equivalent transmit beamforming method is devised based on particle swarm optimization, which incorporates the frequency offset of each subarray and the corresponding weight vector into the optimization problem. With the proposed approach, the array is capable of generating focused beampattern with low sidelobe in both range and angle domains. Numerical results show that the proposed algorithm improves the beamforming performance, range resolution, and sidelobe suppression.
The state of charge (SoC) estimation is the safety management basis of the packing lithium-ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to desc...
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The state of charge (SoC) estimation is the safety management basis of the packing lithium-ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state-space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.
Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameteroptimization (MPO) functions are used to summarize mul...
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Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameteroptimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.
Stream processing is a compute paradigm that promises safe and efficient parallelism. Its realization requires optimization of multipleparameters such as kernel placement and communications. Most techniques to optimi...
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ISBN:
(纸本)9781479989379
Stream processing is a compute paradigm that promises safe and efficient parallelism. Its realization requires optimization of multipleparameters such as kernel placement and communications. Most techniques to optimize streaming systems use queueing network models or network flow models, which often require estimates of the execution rate of each compute kernel. This is known as the non-blocking "service rate" of the kernel within the queueing literature. Current approaches to divining service rates are static. To maintain a tuned application during execution (while online) with non-static workloads, dynamic instrumentation of service rate is highly desirable. Our approach enables online service rate monitoring for streaming applications under most conditions, obviating the need to rely on steady state predictions for what are likely non-steady state phenomena. This work describes an algorithm to approximate non-blocking service rate, its implementation in the open source RaftLib [2] framework, and validates the methodology using streaming applications on multi-core hardware.
In the era of technological advances and Industry 4.0, massive data collection and analysis is a common approach followed by many industries and companies worldwide. One of the most important uses of data mining and M...
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In the era of technological advances and Industry 4.0, massive data collection and analysis is a common approach followed by many industries and companies worldwide. One of the most important uses of data mining and Machine Learning techniques is to predict possible breaks or failures in industrial processes or machinery. This research designs and develops a hyper-heuristic inspired methodology to autonomously identify significant parameters of the time series that characterize the behaviour of relevant process variables enabling the prediction of failures. The proposed hyper-heuristic inspired approach is based on the combination of an optimization process performed by a meta-heuristic algorithm (Harmony Search) and feature based statistical methods for anomaly detection. It demonstrates its adaptability to different failure cases without expert domain knowledge and the capability of autonomously identifying most relevant parameters of the time series to detect the abnormal behaviour prior to the final failure. The proposed solution is validated against a real database of a cold stamping process yielding satisfactory results respect to a novel AUC_ROC based metric, named AUC _ MOD, and other conventional metrics, i.e., Specificity, Sensitivity and False Positive Rate.
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