Fostering an Entrepreneurial Mindset in Systems Simulation

Simulation is tool frequently used by companies when designing systems to evaluate alternative system designs. In particular, simulation is employed when the dynamic behavior of a system is not well understood and the decisions that are being made have significant economic or social impacts. However, courses in systems simulation typically focus on the technical and statistical aspects of model building and the comparison of design alternatives focused on operational performance of the system (that is, performance metrics that can be collected within the simulation itself.) This paper investigates how an entrepreneurial mindset can be fostered through activities/methods that encourage students to look beyond the operational aspects of system design to the overall value and impact of design alternatives. The development, implementation, and outcomes of two KEEN modules are presented to demonstrate the integration of an entrepreneurial mindset in a systems simulation course

Activity Alternative Selection Considering Stochastic Reliability in Project Planning

Activity Alternative Selection Considering Stochastic Reliability in Project Planning. Project management often involves the selection of alternatives from various options for completion of one or more activities. In some cases in addition to uncertainty in cost and activity completion time, the reliability of the alternatives must also be considered. This situation often arises when alternatives involve new or unproven technologies or processes. In this research, we present a stochastic simulation-based optimization method to evaluate project networks to select from among alternatives for completing project activities when several options are available with stochastic completion times and reliability in terms of the likelihood of successfully completing the activity. The objective of this methodology is to optimize the expected time required to complete the project. The output of the method includes a distribution of the project completion time and the configuration of alternatives selected to complete the project

Capacity Analysis of Automated Material Handling Systems in Semiconductor Fabs

A critical aspect of semiconductor manufacturing is the design and analysis of material handling and production control polices to optimize fab performance. As wafer sizes have increased, semiconductor fabs have moved to-ward the use of automated material handling systems (AMHS). However, the behavior of AMHS and the effects of AMHS on fab productivity is not well understood. This research involves the development of a design and analysis methodology for evaluating the throughput capacity of AMHS. A set of simulation experiments is used to evaluate the throughput capacity of an AMHS and the effects on fab performance measures. The analysis uses SEMATECH fab data for full semiconductor fabs to evaluate the AMHS throughput capacity

Relevant Education in Math and Science (REMS): K-12 STEM Outreach Program using Industrial Engineering Applications

Relevant Education in Math and Science (REMS): K-12 STEM Outreach Program using Industrial Engineering Applications. Relevant Education in Math and Science (REMS) is a university-led STEM outreach program designed to use real-world industrial engineering problems to make 5th – 12th grade math and science fun and meaningful for students. In this work, we present the nine current engineering lab activities, developed in both in-lab and on-line format consisting of three different real-world contexts: competitive manufacturing, distribution, and healthcare. These activities are linked to curricular subject standards found in math and science at elementary, middle and high school grade levels. In addition, we present the multi-phased design, development, and assessment and evaluation process that was utilized to produce this program, including the results of over 1,300 surveys completed by students and teachers who have participated in the program activities

Imaging butyrylcholinesterase activity in Alzheimer's disease

No abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55890/1/21023_ftp.pd

Simulation analysis of dispatching rules for automated material handling systems and processing tools in semiconductor fabs

Abstract – A critical aspect of semiconductor manufacturing is the design and analysis of material handling and production control polices to optimize fab performance. This research utilizes two simulations of SEMATECH fab data of actual production fabs. The hypothesis of this study is that both vehicle and machine dispatching rules and their interaction will have significant impact on fab performance. To test this hypothesis, a full factorial design experiment is performed. The vehicle and machine dispatching rules as well as their interaction are shown to have a significant impact

KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-wave Hardware

With the ever-increasing demands of e-commerce, the need for smarter warehousing is increasing exponentially. Such warehouses requires industry automation beyond Industry 4.0. In this work, we use consumer-grade millimeter-wave (mmWave) equipment to enable fast, and low-cost implementation of our localization system. However, the consumer-grade mmWave routers suffer from coarse-grained channel state information due to cost-effective antenna array design limiting the accuracy of localization systems. To address these challenges, we present a Machine Learning (ML) and Kalman Filter (KF) integrated localization system (KF-Loc). The ML model learns the complex wireless features for predicting the static position of the robot. When in dynamic motion, the static ML estimates suffer from position mispredictions, resulting in loss of accuracy. To overcome the loss in accuracy, we design and integrate a KF that learns the dynamics of the robot motion to provide highly accurate tracking. Our system achieves centimeter-level accuracy for the two aisles with RMSE of 0.35m and 0.37m, respectively. Further, compared with ML only localization systems, we achieve a significant reduction in RMSE by 28.5% and 54.3% within the two aisles

Autonomous Vehicles and Machines Conference, at IS&T Electronic Imaging

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset

Detonation of Meta-stable Clusters

We consider the energy accumulation in meta-stable clusters. This energy can be much larger than the typical chemical bond energy (~;;1 ev/atom). For example, polymeric nitrogen can accumulate 4 ev/atom in the N8 (fcc) structure, while helium can accumulate 9 ev/atom in the excited triplet state He2* . They release their energy by cluster fission: N8 -> 4N2 and He2* -> 2He. We study the locus of states in thermodynamic state space for the detonation of such meta-stable clusters. In particular, the equilibrium isentrope, starting at the Chapman-Jouguet state, and expanding down to 1 atmosphere was calculated with the Cheetah code. Large detonation pressures (3 and 16 Mbar), temperatures (12 and 34 kilo-K) and velocities (20 and 43 km/s) are a consequence of the large heats of detonation (6.6 and 50 kilo-cal/g) for nitrogen and helium clusters respectively. If such meta-stable clusters could be synthesized, they offer the potential for large increases in the energy density of materials