Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Considerations on design, development and testing of Electrical Machines for automotive HVAC

HVAC represents one of the highest energy consumer in a vehicle and for full electric vehicles, the design of HVAC and the dimensioning of its driving system is of utmost importance in order to avoid the limitation of driving range. Due to its advantages, especially when it comes to power density, PMSM is one of the most used electrical machines for a wide range of automotive applications, including HVAC systems. The paper presents the generation of the requirements, design, analysis and HiL testing of a PMSM for HVAC applications. The challenge is to develop a machine answering to the requirements for an electric vehicle HVAC at low- voltage. An experimental model of the machine was tested using a test- bench, based on HiL techniques

Considerations on design, development and testing of Electrical Machines for automotive HVAC

HVAC represents one of the highest energy consumer in a vehicle and for full electric vehicles, the design of HVAC and the dimensioning of its driving system is of utmost importance in order to avoid the limitation of driving range. Due to its advantages, especially when it comes to power density, PMSM is one of the most used electrical machines for a wide range of automotive applications, including HVAC systems. The paper presents the generation of the requirements, design, analysis and HiL testing of a PMSM for HVAC applications. The challenge is to develop a machine answering to the requirements for an electric vehicle HVAC at low- voltage. An experimental model of the machine was tested using a test- bench, based on HiL techniques

Multiphysics Modeling of an Permanent Magnet Synchronous Machine

This paper analyzes the noise and vibration in PMSMs. There are three types of vibrations in electrical machines: electromagnetic,mechanical and aerodynamic. Electromagnetic force are the main cause of noise and vibration in PMSMs. It is very important to calculate precisely the natural frequencies of the stator system. If oneradial force (which are the main cause for electromagnetic vibration) has the frequency close to the natural frequency of the stator system for the same order of vibrational mode, then this force canproduce dangerous vibration in the stator system. The natural frequencies for a stator system of a PMSM have been calculated. Finally a Structural Analysis has been made , pointing out the radialdisplacement and stress for the chosen PMSM

System-level Modeling and Simulation of a Permanent Magnet Synchronous Motor for an Integrated Starter Alternator

This paper first presents an overview of various hybrid electric vehicle architectures with an emphasis on the Integrated Starter Alternator (ISA) hybrid architecture. The operation modes of an ISAsystem and the constraints for an electric machine acting as an ISA are detailed. Finally a model for a ISA hybrid vehicle, developed in AMESim is presented and the results of a simulated drive cycle are assessed

Towards Controlled Transmission: A Novel Power-Based Sparsity-Aware and Energy-Efficient Clustering for Underwater Sensor Networks in Marine Transport Safety

Energy-efficient management and highly reliable communication and transmission mechanisms are major issues in Underwater Wireless Sensor Networks (UWSN) due to the limited battery power of UWSN nodes within an harsh underwater environment. In this paper, we integrate the three main techniques that have been used for managing Transmission Power-based Sparsity-conscious Energy-Efficient Clustering (CTP-SEEC) in UWSNs. These incorporate the adaptive power control mechanism that converts to a suitable Transmission Power Level (TPL), and deploys collaboration mobile sinks or Autonomous Underwater Vehicles (AUVs) to gather information locally to achieve energy and data management efficiency (Security) in the WSN. The proposed protocol is rigorously evaluated through extensive simulations and is validated by comparing it with state-of-the-art UWSN protocols. The simulation results are based on the static environmental condition, which shows that the proposed protocol performs well in terms of network lifetime, packet delivery, and throughput

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press