A Hybrid Strategy for Integrating Sensor Information

2014 ◽  
Keyword(s):  
Hybrid Strategy ◽  
Sensor Information

Hybrid Strategy for Evaluating On-orbit Servicing, Assembly, and Manufacturing Technologies

ASCEND 2020 ◽  
2020 ◽  
Author(s):  
Justin C. Brannan ◽  
Craig R. Carignan ◽  
Brian J. Roberts
Keyword(s):  
Hybrid Strategy ◽  
Manufacturing Technologies

Systematic Design and Evaluation of Drug Repurposing-Oriented Alzheimer’s Disease Ontology (Preprint)

2020 ◽  
Author(s):  
Fang Li ◽  
Muhammad "Tuan" Amith ◽  
Grace Xiong ◽  
Jingcheng Du ◽  
Yang Xiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Essential Elements ◽  
Genomic Research ◽  
Core Knowledge ◽  
Disease Ontology ◽  
Hybrid Strategy ◽  
Complementary Strategy

BACKGROUND Alzheimer’s Disease (AD) is a devastating neurodegenerative disease, of which the pathophysiology is insufficiently understood, and the curative drugs are long-awaited to be developed. Computational drug repurposing introduces a promising complementary strategy of drug discovery, which benefits from an accelerated development process and decreased failure rate. However, generating new hypotheses in AD drug repurposing requires multi-dimensional and multi-disciplinary data integration and connection, posing a great challenge in the era of big data. By integrating data with computable semantics, ontologies could infer unknown relationships through automated reasoning and fulfill an essential role in supporting computational drug repurposing. OBJECTIVE The study aimed to systematically design a robust Drug Repurposing-Oriented Alzheimer’s Disease Ontology (DROADO), which could model fundamental elements and their relationships involved in AD drug repurposing and integrate their up-to-date research advance comprehensively. METHODS We devised a core knowledge model of computational AD drug repurposing, based on both pre-genomic and post-genomic research paradigms. The model centered on the possible AD pathophysiology and abstracted the essential elements and their relationships. We adopted a hybrid strategy to populate the ontology (classes and properties), including importing from well-curated databases, extracting from high-quality papers and reusing the existing ontologies. We also leveraged n-ary relations and nanopublication graphs to enrich the object relations, making the knowledge stored in the ontology more powerful in supporting computational processing. The initially built ontology was evaluated by a semiotic-driven and web-based tool Ontokeeper. RESULTS The current version of DROADO was composed of 1,021 classes, 23 object properties and 3,207 axioms, depicting a fundamental network related to computational neuroscience concepts and relationships. Assessment using semiotic evaluation metrics by OntoKeeper indicated sufficient preliminary quality (semantics, usefulness and community-consensus) of the ontology. CONCLUSIONS As an in-depth knowledge base, DROADO would be promising in enabling computational algorithms to realize supervised mining from multi-source data, and ultimately, facilitating the discovery of novel AD drug targets and the realization of AD drug repurposing.

scholarly journals Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter

2021 ◽  
Vol 11 (3) ◽  
pp. 1211
Author(s):  
En-Chih Chang ◽  
Chun-An Cheng ◽  
Rong-Ching Wu
Keyword(s):  
Steady State ◽  
Digital Signal Processor ◽  
Tracking Control ◽  
Harmonic Distortion ◽  
Digital Signal ◽  
Fast Convergence ◽  
Solution Quality ◽  
Hybrid Strategy ◽  
Optimal Tracking ◽  
Optimal Tracking Control

This paper develops a full-bridge DC-AC converter, which uses a robust optimal tracking control strategy to procure a high-quality sine output waveshape even in the presence of unpredictable intermissions. The proposed strategy brings out the advantages of non-singular fast convergent terminal attractor (NFCTA) and chaos particle swarm optimization (CPSO). Compared with a typical TA, the NFCTA affords fast convergence within a limited time to the steady-state situation, and keeps away from the possibility of singularity through its sliding surface design. It is worth noting that once the NFCTA-controlled DC-AC converter encounters drastic changes in internal parameters or the influence of external non-linear loads, the trembling with low-control precision will occur and the aggravation of transient and steady-state performance yields. Although the traditional PSO algorithm has the characteristics of simple implementation and fast convergence, the search process lacks diversity and converges prematurely. So, it is impossible to deviate from the local extreme value, resulting in poor solution quality or search stagnation. Thereby, an improved version of traditional PSO called CPSO is used to discover global optimal NFCTA parameters, which can preclude precocious convergence to local solutions, mitigating the tremor as well as enhancing DC-AC converter performance. By using the proposed stable closed-loop full-bridge DC-AC converter with a hybrid strategy integrating NFCTA and CPSO, low total harmonic distortion (THD) output-voltage and fast dynamic load response are generated under nonlinear rectifier-type load situations and during sudden load changes, respectively. Simulation results are done by the Matlab/Simulink environment, and experimental results of a digital signal processor (DSP) controlled full-bridge DC-AC converter prototype confirm the usefulness of the proposed strategy.

scholarly journals A Hybrid Methodology to Minimize Freshwater Consumption during Shrimp Shell Waste Valorization Combining Multi-Contaminant Pinch Analysis and Superstructure Optimization

Polymers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1887
Author(s):  
Viviana Quintero ◽  
Arturo Gonzalez-Quiroga ◽  
Angel Darío Gonzalez-Delgado
Keyword(s):  
Process Integration ◽  
Preliminary Design ◽  
Water Network ◽  
Hybrid Strategy ◽  
Shrimp Shell ◽  
Optimal Value ◽  
Shell Waste ◽  
Programming Techniques ◽  
Shrimp Shell Waste ◽  
Freshwater Consumption

The conservation and proper management of natural resources constitute one of the main objectives of the 2030 Agenda for Sustainable Development designed by the Member States of the United Nations. In this work, a hybrid strategy based on process integration is proposed to minimize freshwater consumption while reusing wastewater. As a novelty, the strategy included a heuristic approach for identifying the minimum consumption of freshwater with a preliminary design of the water network, considering the concept of reuse and multiple pollutants. Then, mathematical programming techniques were applied to evaluate the possibilities of regeneration of the source streams through the inclusion of intercept units and establish the optimal design of the network. This strategy was used in the shrimp shell waste process to obtain chitosan, where a minimum freshwater consumption of 277 t/h was identified, with a reuse strategy and an optimal value of US $5.5 million for the design of the water network.

scholarly journals Design and Implementation of Track and Field Training Information Collection and Feedback System Based on Multi-sensor Information Fusion

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ling Li ◽  
Chengliang Li
Keyword(s):  
Information Fusion ◽  
Feedback System ◽  
Track And Field ◽  
Information Collection ◽  
Control Group ◽  
Average Performance ◽  
Field Training ◽  
Sensor Information ◽  
Experimental Group ◽  
Field Sports

AbstractTrack and field sports are known as the "mother of sports". Whether in the field of athletics, fitness, or education, modern track and field sports have developed rapidly. The field of athletics has reached the point where it challenges the limits of humans. The development of China is inseparable from the support of science and technology, and it is inseparable from human scientific research on track and field sports. In order to improve the scientific level of track and field training methods and develop our country's sports industry, this paper designs a track and field training information collection and feedback system based on multi-sensor information fusion. In the method part, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, using weight coefficient fusion method, D-S evidence theory algorithm and Kalman filter algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, and data record analysis on this system. By designing the experimental group and the control group, it can be seen that the average performance of the two groups of athletes in the 50-meter run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine.

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information
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