Minimization of clock network is traditionally achieved by clock routing, which may be helpless for a poor placement result. In this paper, a novel Dynamic Clock-Tree Building technique integrated into placement for zero-skew design is proposed. This method combines a pre-designed clock-tree with the Force-Directed Placement procedure to navigate the register placement for minimizing the clock network. Meanwhile, a new model of Multi-Level Bounding Box and technique of Multi-Level Attractive Force are proposed to give a better local distribution of registers. Experiments on several standard-cell benchmarks indicate an average 26.1% clock network reduction with the logic cell placement preserved well.
Prognosis of the Remaining Useful Life (RUL) of a unit or system plays an important role in system reliability analysis and maintenance decision making. One key aspect of the RUL prognosis is the construction of the best prediction interval for failure occurrence. The interval should have a reasonable length and yield the best prediction power. In current practice, the center-based interval and traditional confidence interval are widely used. Although both are easy to construct, they do not provide the best prediction performance. In this article, we propose a new scheme, the Maximum Power Interval (MPI), for estimating the interval with maximum prediction power. The MPI guarantees the best prediction power under a given interval length. Some technical challenges involved in the MPI method were resolved using the maximum entropy principle and truncation method. A numerical simulation study confirmed that the MPI has better prediction power than other prediction intervals. A case study using a real industry data set was conducted to illustrate the capability of the MPI method.
Adaptive detection of range-spread target is discussed in the extremely training-deficient scenarios. The performance analysis of the detector without training data is conducted by Monte Carlo simulation. It implies that, the detector performs robustly for different correlations of clutter and for different target scatterer models. Furthermore, the detection performance improves as the number of channels used decreases or the range extent of target increases. In addition, the detection performance decreases as the fluctuation degree or the correlation degree of target scatterers strengthens for high detection probability, while increasing with weakening fluctuation or correlation for low detection probability.
In this paper, we present a top-down global placement algorithm considering wire density uniformity for CMP variation control. The proposed algorithm is based on top-down recursive bisection framework. Wire weight balancing constraint is employed into bisection to consider wire density uniformity. A multilevel hypergraph partitioning satisfying balancing constraints on not only cell area but also wire weight is performed to acquire more uniform wire distribution. Empirical wire weight model is used to estimate wire density distribution before each bisection of a placement bin. Experimental results show that our algorithm improves ROOSTER [1] with more uniform wire distribution by 3.1% on average and limited increment of wire length by 3.0%.
In this paper, we propose a critical-path based timing driven FastPlace, named TimFastPlace, which uses an iterative critical path-based weighting model to optimize the critical path delay at the equation solving stage. Experimental results on several industry cases and ISCAS89 cases show that we are able to obtain up to 30.83% Worst Negative Slack (WNS), an average of 23.42% WNS and 18.87% Total Negative Slack (TNS) improvement in circuit delays at an average of 2.54% wire length increase. Besides, runtime is kept at the same level as FastPlace.
The technology of amine-based carbon dioxide (CO2) capture has been widely adopted for reducing CO2 emissions and mitigating global warming. The primary research objective in the field of post-combustion CO2 capture process system is to improve effectiveness and efficiency of the process. Extensive literature review of the research showed that the dominant approach was to investigate the behaviors of the aqueous amine solvents for enchancing CO2 capture efficiency. As the operation of an amine-based CO2 capture system is complicated and involves monitoring over one hundred process parameters and careful manipulation of numerous valves and pumps, automated monitoring and process control can be a fruitful approach to enhance efficiency of the CO2 capture process system. In this study, artificial intelligence techniques were applied for development of a knowledge-based expert system that effectively monitors and controls the CO2 capture process system so as to enhance CO2 capture efficiency. The Knowledge-Based System for Carbon Dioxide Capture (KBSCDC) was implemented with DeltaV Simulate (trademark of Emerson Corp., USA). DeltaV Simulate provides control utilities and algorithms which support the configuration of control strategies in modular components. The KBSCDC can conduct real-time monitoring and diagnosis, as well as suggest remedies for any abnormality detected. Also, the control strategies applied to the control devices of the process are simulated in KBSCDC. The expert system enhances performance and efficiency of the CO2 capture process system because it supports automated diagnosis of the system should any abnormal conditions occur. In this way, costly downtime and maintenance are avoided.
In this paper, we obtain the supersolvability for a finite group based on the assumption that minimal subgroups and cyclic subgroups of order 4 have the semi cover-avoiding properties. Some known results are generalized. Mathematics Subject Classification: 20D10, 20D20
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
