A Hierarchical Optimization Approach to Optimal Production Scheduling in an Industrial Continuous Olefin Polymerization Reactor

2009 ◽  
Vol 3 (1) ◽  
pp. 36-46 ◽  
Author(s):  
Christos Chatzidoukas ◽  
Stratos Pistikopoulos ◽  
Costas Kiparissides
Keyword(s):  
Production Scheduling ◽  
Olefin Polymerization ◽  
Hierarchical Optimization ◽  
Optimization Approach ◽  
Optimal Production ◽  
Polymerization Reactor

scholarly journals Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1955
Author(s):  
Md Jubaer Hossain Pantho ◽  
Pankaj Bhowmik ◽  
Christophe Bobda
Keyword(s):  
Power Consumption ◽  
High Speed ◽  
Image Sensor ◽  
Machine Learning Algorithms ◽  
Hierarchical Optimization ◽  
Computation Method ◽  
Optimization Approach ◽  
Efficient Computation ◽  
Feature Maps ◽  
Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

Optimal production scheduling for smart manufacturers with application to food production planning

2020 ◽  
Vol 84 ◽  
pp. 106609
Author(s):  
Farhad Angizeh ◽  
Hector Montero ◽  
Abhinay Vedpathak ◽  
Masood Parvania
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Food Production ◽  
Optimal Production

System-Level Optimization Model for a Snake-Inspired Robot Based on a Rectilinear Gait

2008 ◽  
Author(s):  
James K. Hopkins ◽  
Brent W. Spranklin ◽  
Satyandra K. Gupta
Keyword(s):  
Optimization Model ◽  
Equations Of Motion ◽  
Optimal Performance ◽  
System Level ◽  
Hierarchical Optimization ◽  
Physical Parameters ◽  
Optimization Approach ◽  
Gait Parameters ◽  
Robot Performance

Physical parameters of modules and gait parameters affect the overall snake-inspired robot performance. Hence the system-level optimization model has to concurrently optimize the module parameters and the gait. The equations of motion associated with the rectilinear gait are quite complex due to the changing topology of the rectilinear gait. Embedding these equations in the system-level optimization model leads to a computationally challenging formulation. This paper presents a system-level optimization model that utilizes a hierarchical optimization approach and meta-models of the pre-computed optimal gaits to reduce the complexity of the optimization model. This approach enabled us to use an experimentally validated physics-based model of the rectilinear gait and yet at the same time enabled us to create a system-level optimization model with a manageable complexity. A detailed case study is presented to show the importance of concurrently optimizing the module parameters and the gait using our model to obtain the optimal performance for a given mission.

scholarly journals Optimal production scheduling of food process industries

2020 ◽  
Vol 134 ◽  
pp. 106682 ◽  
Author(s):  
Georgios P. Georgiadis ◽  
Borja Mariño Pampín ◽  
Daniel Adrián Cabo ◽  
Michael C. Georgiadis
Keyword(s):  
Production Scheduling ◽  
Process Industries ◽  
Optimal Production
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