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- Xu, Feng, et al.
(författare)
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Broadband spectroscopic sensor for real-time monitoring of industrial SO2 emissions
- 2007
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Ingår i: Applied Optics. - 2155-3165. ; 46:13, s. 2503-2506
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Tidskriftsartikel (refereegranskat)abstract
- A spectroscopic system for continuous real-time monitoring of SO2, concentrations in industrial emissions was developed. The sensor is well suited for field applications due to simple and compact instrumental design, and robust data evaluation based on ultraviolet broadband absorption without the use of any calibration cell. The sensor has a detection limit of 1 ppm, and was employed both for gas-flow simulations with and without suspended particles, and for in situ measurement of SO2 concentrations in the flue gas emitted from an industrial coal-fired boiler. The price/performance ratio of the instrument is expected to be superior to other comparable real-time monitoring systems.
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2. |
- Shen, Zhaoyan, et al.
(författare)
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A Multiagent Reinforcement Learning-Assisted Cache Cleaning Scheme for DM-SMR
- 2023
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Ingår i: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0278-0070 .- 1937-4151. ; 42:8, s. 2500-2513
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Tidskriftsartikel (refereegranskat)abstract
- To support nonsequential writes, persistent cache (PC) is constructed in drive managed SMR (DM-SMR) drive. However, PC cleaning introduces drastic performance degradation and enlarges tail latencies. In this article, we propose to utilize reinforcement learning (RL) to mitigate the longtail latency of PC cleaning. Our scheme uses the lightweight Q-learning method to monitor and learn the idle time of I/O workloads, based on which PC cleaning is intelligently guided, thus maximally exploit idle time between requests and hiding tail latency from normal requests. In addition, a multiagent RL scheme with clustering algorithm is adopted to further mitigate the tail latencies and adapt to variable workloads. We emulate a DM-SMR drive inside a Linux device driver to implement our proposed scheme. According to the experimental results, our scheme can effectively reduce the tail latency by 59.45% at the 99.9th percentile and the average latency by 48.75% compared with a typical shingled magnetic recording (SMR) design.
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