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Innovative design of cooling system for a high-torque electric machine integrated with power electronics
Get PDFThe growth of electrical machine applications in high-torque environments such as marine propulsion and wind energy is encouraging the development of higher-power-density machines at ever higher efficiencies and under competitive pressure to meet higher demands. In this study, numerical simulations are performed to investigate the characteristics of air cooling applied to a 3 MW high-torque internal permanent magnet electric machine with integrated power electronics. The whole system of the main machine and two converters at either end are modelled with all details. Effects of different parameters on the total pressure drop and air flow rate to the machine and converters are examined. Results show that by changing the converter outlet hole size, the air flow rate to the machine and converter can be adjusted. Air guides and pin vents reveal excellent performance in the distribution of air to laminations and windings with a penalty of some increase in pressure drop, which is more pronounced when using smaller outlet holes. Furthermore, the air return manifold increases the pressure drop and causes a reduction in air flow rate to the converter. Insulation between compression plate and laminations is an unavoidable component used in electric machines and acts as a thermal insulator. However, it can also significantly augment pressure drop, especially in combination with smaller outlet holes. Thermal studies of the integrated power electronics illustrate that components’ temperatures are less than the temperature limit, confirming enough air through the converter. Analysis of power electronics in the case of fan failure provides the operational time window for the operators to respond
Augmenting scenario description languages for intelligence testing of automated driving systems
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A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data
Get PDFTracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks
Rapid beam training at terahertz frequency with contextual multi-armed bandit learning
Get PDFTerahertz (THz) frequency technology holds great promise for enabling high data rates and low latency, essential for manufacturing applications within Industry 4.0. To achieve these, beam training is necessary to enable MIMO communications without the need for explicit channel state information (CSI). In this context, the Multi-Armed Bandit (MAB) algorithms are able to facilitate online learning and decision-making in beam training, eliminating the necessity for extensive offline training and data collection. In this paper, we introduce three algorithms to investigate the applications of MAB in beam training at Terahertz frequency: UCB, Loc-LinUCB, and Probing-LinUCB. While UCB builds upon the well-established Upper Confidence Bound algorithm, Loc-LinUCB and Probing-LinUCB utilize the location of the user equipment (UE) and probing information to enhance decision-making, respectively. The beam training protocols for each algorithm are also detailed. We evaluate the performance of these algorithms using data generated by the DeepMIMO framework, which simulates abrupt changes and various challenging characteristics of wireless channels encountered in realistic scenarios as UEs move. The results illustrate that Loc-LinUCB and Probing-LinUCB outperform UCB, showing the potential of leveraging contextual MAB for beam training in Terahertz communications
Fetal origins of mental health : evidence from Africa
No full textMental health disorders are a substantial portion of the global disease burden, and the treatment gap is higher in developing countries. Accounting for location and year of birth fixed effects, we find temperature shocks in utero increase depressive symptoms in adulthood using data on 19 African countries. Impacts are present for several depressive symptoms, and are greatest in younger cohorts
Conditioning and associative learning
No full textAssociative learning is the process whereby humans and other animals learn the predictive relationship between cues in their environment. This process underlies simple forms of learning from rewards, such as classical and operant conditioning. In this chapter, we introduce the basics of associative learning and discuss the role that memory processes play in the establishment and maintenance of this learning. We then discuss the role that associative learning plays in human memory, including through paired associate learning, the enhancement of memory by reward, and the formation of episodic memories. Finally, we illustrate how the memory process influences choice in decision-making, where associative learning allows people to learn the values of different options. We conclude with some suggestions about how models of associative learning, memory, and choice can be integrated into a single theoretical framework
The ethics of influence in state-regulated schools : Tillson v Rawls.
No full textJohn Tillson’s excellent Children, Religion and the Ethics of Influence develops and deploys the ‘epistemic criterion’ for deciding whether teachers should promote belief in particular propositions. He defends that criterion by arguing that it promotes human well-being and enables individuals to fulfil their duty to pursue the truth. In this article I draw on John Rawls’s conception of political liberalism to suggest that the
epistemic criterion is an inappropriate basis for the political community’s shaping of children’s beliefs
