Early Design Space Exploration Framework for Memristive Crossbar Arrays

For memristive crossbar arrays, currently, no high-level design validation and early space exploration tools exist in the literature. Such tools are essential to quickly verify the design functionality as well as compare design alternatives in terms of power and performance. In this work, we propose a VHDL-based framework that enables us to quickly perform behavioral simulation as well as estimate dynamic energy consumption and speed of any large memristive crossbar array. We propose a high-level (VHDL) model of a memristor based on which crossbar architectures can be modeled. The individual memristor model is embedded with power and delay numbers obtained from a detailed memristor model. We demonstrate the framework for MAGIC-style memristive crossbars. We validate the framework against detailed Verilog-A based model on fifteen combinational benchmarks. For the single row model, we obtained 153x simulation speedup over HSPICE, average estimation errors of 6.64% and 0% for dynamic energy consumption and cycle-time, respectively. For the transpose model, we obtained average estimation errors of 5.51% and 10.90% for dynamic energy consumption and cycle-time, respectively. We also extend our framework to support another prominent logic style and validate through a case study. The proposed framework can be easily extended to other emerging technologies.

Bending Metal: Improving Sheet Metal Repair at Tobyhanna Army Depot through Lean Six Sigma

The Army's Lean Six Sigma methodology includes five phases: Define, Measure, Analyze, Improve, and Control (DMAIC); each of these phases includes interaction between the stakeholder and process team. This paper focuses on the application of Lean Six Sigma methodology at Tobyhanna Army Depot to help reduce overruns and repair cycle time within the sheet metal cost center. At the initiation of the project, the process incurred over 4,000 hours of overruns, a situation in which it takes longer to repair an asset than the standard hours allocated for the repair. Additionally, the average repair cycle time, amount of time required to repair an individual asset, exceeded customer expectations by almost four days. The paper describes recommended solutions to address both problems.

Optimizing Cycle Time of Industrial Robotic Tasks with Multiple Feasible Configurations at the Working Points

Industrial robot applications should be designed to allow the robot to provide the best performance for increasing throughput. In this regard, both trajectory and task order optimization are crucial, since they can heavily impact cycle time. Moreover, it is very common for a robotic application to be kinematically or functionally redundant so that multiple arm configurations may fulfill the same task at the working points. In this context, even if the working cycle is composed of a small number of points, the number of possible sequences can be very high, so that the robot programmer usually cannot evaluate them all to obtain the shortest possible cycle time. One of the most well-known problems used to define the optimal task order is the Travelling Salesman Problem (TSP), but in its original formulation, it does not allow to consider different robot configurations at the same working point. This paper aims at overcoming TSP limitations by adding some mathematical and conceptual constraints to the problem. With such improvements, TSP can be used successfully to optimize the cycle time of industrial robotic tasks where multiple configurations are allowed at the working points. Simulation and experimental results are presented to assess how cost (cycle time) and computational time are influenced by the proposed implementation.

Opportunities and Challenges of Nanoparticles in Digestive Tumours as Anti-Angiogenic Therapies

Digestive tumours, a common kind of malignancy worldwide, have recently led to the most tumour-related deaths. Angiogenesis, the process of forming novel blood vessels from pre-existing vessels, is involved in various physiological and pathological processes in the body. Many studies suggest that abnormal angiogenesis plays an important role in the growth, progression, and metastasis of digestive tumours. Therefore, anti-angiogenic therapy is considered a promising target for improving therapeutic efficacy. Traditional strategies such as bevacizumab and regorafenib can target and block the activity of proangiogenic factors to treat digestive tumours. However, due to resistance and some limitations, such as poor pharmacokinetics, their efficacy is not always satisfactory. In recent years, nanotechnology-based anti-angiogenic therapies have emerged as a new way to treat digestive tumours. Compared with commonly used drugs, nanoparticles show great potential in tumour targeted delivery, controlled drug release, prolonged cycle time, and increased drug bioavailability. Therefore, anti-angiogenic nanoparticles may be an effective complementary therapy to treat digestive tumours. In this review, we outline the different mechanisms of angiogenesis, the effects of nanoparticles on angiogenesis, and their biomedical applications in various kinds of digestive tumours. In addition, the opportunities and challenges are briefly discussed.

A Comparative Study of Unbalanced Production Lines Using Simulation Modeling: A Case Study for Solar Silicon Manufacturing

In the solar silicon manufacturing industry, the production time for crystal growth is ten times longer than at other workstations. The pre-processing time at the ingot-cutting station causes work-in-process (WIP) accumulation and an excessively long cycle time. This study aimed to find the most effective production system for reducing WIP accumulation and shortening the cycle time. The proposed approach considered pull production systems, and the response surface methodology was adopted for performance optimization. A simulation-based optimization technique was used for determining the optimal pull production system. The comparison between the results of various simulated pull production systems and those of the existing solar silicon manufacturing system showed that a hybrid production system in which a kanban station was installed before the bottleneck station with a CONWIP system incorporated for the rest of the production line could reduce the WIP volume by 26% and shorten the cycle time by 16% under the same throughput conditions.

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins.