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Digital Systems
Published in Wai-Kai Chen, Analog and VLSI Circuits, 2018
Festus Gail Gray, Wayne D. Grover, Josephine C. Chang, Bing J. Sheu, Roland Priemer, Rung Yao, Flavio Lorenzelli
The pipeline can be increased by increasing the number of latches internal to the feedback loops. The computational latency associated with the internal feedback prevents one from introducing pipeline simply by inserting latches on feedforward cutsets. In fact, inserting latches in the loop would change the overall transfer function. This difficulty can be overcome by recasting the algorithm into an equivalent formulation from an I-O point of view. The transformations applied to the algorithm, prior to the mapping, have the purpose of creating additional concurrency, thereby increasing the achievable throughput rate. Without ever changing the algorithm’s transfer function, additional delays are introduced inside the recursive loop. These delays are subsequently used for pipelining. In the sequel, we briefly describe two types of look-ahead techniques that generate the desired algorithmic transformations, namely the clustered and the scattered look-ahead techniques proposed by Loomis and Sinha [18] and Parhi and Messerschmitt [19], respectively. Look-ahead techniques are based on successive iterations of the basic recursion, in order to generate the desired level of concurrency. The implementation is then based on the iterated version of the algorithm.
Integrated Process Planning and Scheduling Using Dynamic Approach
Published in Rakesh Kumar Phanden, Ajai Jain, J. Paulo Davim, Integration of Process Planning and Scheduling, 2019
Gideon Halevi, Rakesh Kumar Phanden
The cycle of scheduling commences by scanning every resource when searching for a resource that is free. A resource that is free goes through every operation and the free operation is put down. The best resource operation could be based on the objectives of performance like minimum processing cost or time. The results of scanning with a candidate list for loading using the following rules are: Give priority to critical items and all their operations. Use the look-ahead function.If an item quantity is below xxx attempt to combine orders.If the seceding operation of the item that the resource has just finished is (not best) but economical than giving priority.If the patch comprises a single entry only, then the functionality is put on the resource.If the patch has more than a single entity, then the system puts forth operations with the biggest gap of time for processing it on a resource that is different.If there is a list that is vacant, this infers that there is no free best operation which is available for resource processing. Hence, the resource gets to be idle.
Compressors
Published in Roey Izhaki, Mixing Audio, 2017
Compression can be tricky with sharp level changes, like those of transients. In order to contain transients, a compressor needs a very fast response, but this is not always possible. For one, the gain stage of some compressors, optical ones for instance, is often not fast enough to catch these transients. Then, even if a compressor offers fast response times, the quick clamping down of signals might not produce musical results. It would be great if the side-chain could see the input signal slightly in advance so it could have more time to react to transients. The look-ahead function enables this.
Methodology to determine the impact of simplified building models on model-predictive-control morning start optimization performance
Published in Science and Technology for the Built Environment, 2018
The MPC uses a variable length timestep look ahead (n) out to the horizon. The look ahead period is activated at 5:45 am, to perform predictions for 6:00 am which corresponds to RBC, and runs through 7:45 am. A feed-forward analysis of temperature options is conducted until 8:00 am to determine the optimal start time, in 15-minute increments. The MPC optimizer function is to minimize Equation 1 (minimize energy consumption), subject to the constraint of Equation 2 (constraint on the building average temperature [T] at 8:00 am to ensure occupant comfort). This corresponds to a n = 9 step look ahead starting at 5:45 am, down to a single look ahead of n = 1 at 7:30 am. The number of options tested equal to the number of timesteps between the current time and 8:00 am. There are only two options available to the system—unoccupied mode TUNOCC (15.6°C and 26.7°C, AHU off) or occupied mode TOCC (21°C and 24°C, AHU on). An example of the set-point options are shown in Table 2. A rule was added to the optimizer that if the occupied set-point are implemented in the transition period, they stay in place to prevent unnecessary equipment cycling. The overnight period can turn on the AHU systems to maintain the setback temperatures.
Coupled lateral-longitudinal vehicle dynamics and control design with three-dimensional state portraits
Published in Vehicle System Dynamics, 2019
The path-following function is utilised to align the corner radius constraint with the actual roadway geometry. Due to initial conditions or error during the cornering process, the path radius may need to be tightened or expanded to the bring the vehicle back onto the path. This adjustment is provided by the path-following function via look-ahead. The vehicle position at a future time step, and , is predicted using a function: where N, E, and ψ are the current global position and heading of the vehicle, respectively.
An integer programming formulation and iterative scheme algorithm for the block relocation problem
Published in Journal of Control and Decision, 2023
To our knowledge, Kim and Hong (2006) published the first formal study of the BRP in the literature. They proposed a depth-first branch-and-bound (B&B) algorithm and a greedy heuristic based on expected values of future relocations for the BRP. Caserta et al. (2009) proposed a binary matrix representation of configurations. This representation works well for the RBRP, but not necessarily for the UBRP. Then, they proposed a greedy look-ahead heuristic algorithm. Caserta et al. (2012) established binary integer programming (IP) formulations for the RBRP and the UBRP, respectively, and proposed a simple heuristic based upon a set of relocation rules. Zhu et al. (2012) proposed two new lower bounds that apply to the RBRP and a novel A* algorithm as well as several greedy heuristics for the RBRP and the UBRP. Petering and Hussein (2013) established a more compact mixed integer programming (MIP) formulation. This formulation, while reducing some of integer variables and expanding its solution ability, has a rather limited solution capability. Zehendner and Feillet (2014) proposed a column generation approach embedded in a branch-and-price procedure for the RBRP. It can solve instances of Caserta et al. (2011) for 4 stacks, 16 blocks and below. Expósito-Izquierdo et al. (2014) proposed two exact A*-based search algorithms for the UBRP and the RBRP, respectively, to solve small scale instances. Meanwhile, they proposed a domain-specific knowledge-based heuristic algorithm to solve large scale instances. Its core idea is to select the position with the lowest probability of needing to be relocated again in the future for the relocated container. Expósito-Izquierdo et al. (2015) proposed an alternative optimisation model and developed a B&B algorithm for the RBRP. Tanaka and Mizuno (2015) and Tanaka (2015) designed two B&B algorithms by using their derived dominance properties for reducing the solution space in a search tree for the UBRP. Tanaka and Takii (2016) presented a tighter lower bound and a faster B&B algorithm for the RBRP. A recent BB for UBRP is developed by Tricoire et al. (2018), which incorporates fast heuristics and a new lower bound. de Melo da Silva et al. (2018) established two new IP formulations for the BRP and called them BRP-m1 and BRP-m2. Despite they improved solution ability, formulations can solve only 181 out of 520 benchmark instances within an hour. Bacci et al. (2020) proposed an exact branch-and-cut algorithm for the RBRP, based on a IP formulation. Lu et al. (2020) proposed a general framework for analysing lower bounds for several variants of the UBRP and a MIP-based iterative algorithm.