Artificial Intelligence and Future of Systems Engineering

2021 ◽  
pp. 47-59
Author(s):  
Thomas A. McDermott ◽  
Mark R. Blackburn ◽  
Peter A. Beling
Keyword(s):  
Artificial Intelligence ◽  
Systems Engineering

CONCEPTUAL DESIGN OF BOMB DETECTOR AS ANTICIPATION OF TERRORISM THREAT TO PUBLIC SERVICES

2021 ◽  
Vol 7 (1) ◽  
pp. 170
Author(s):  
Iris Sumariyanto ◽  
Asep Adang Supriyadi ◽  
I Nengah Putra A
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Design ◽  
Systems Engineering ◽  
Public Services ◽  
Mass Casualties ◽  
Intelligence System ◽  
Three Stages ◽  
Metal Detectors ◽  
Security Surveillance ◽  
Processing Device

<p>Acts of terrorism are crimes and serious violations of human rights, also the threat of violence that can cause mass casualties and destruction of vital strategic objects. This is an urgent threat that needs to be prepared by designing a bomb detector conceptual design as anticipation of the threat of terrorism in public services. This study aims to obtain operational requirements and conceptual design of bomb detectors as detection of terrorism threats in public services. This study uses a mixed-method with a systems engineering approach and a life cycle model to produce a technological design. The results of operational requirements are sensors, standards, artificial intelligence, integration capability, reliability, calibration mode, portable, and easy to maintain. The configuration design is divided into three stages, namely, 1) sensors including a camera security surveillance system vector image, metal detectors, explosive detectors, and A-jamming; 2) as a processing device, processes an order with the help of an artificial intelligence system; and 3)  a security computer (surveillance), early warning, and mobile information to provide information to related agencies, especially the anti-terror unit.</p>

scholarly journals Foreword by Guest Editor LTC James H. Schreiner, PhD, PMP, CPEM

2019 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
James Schreiner
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Text Mining ◽  
Decision Analysis ◽  
Modeling And Simulation ◽  
Systems Engineering ◽  
West Point ◽  
United States Military Academy ◽  
United States Military ◽  
Military Academy

This special issue of the Industrial and Systems Engineering Review highlights top papers from the 2019 annual General Donald R. Keith memorial capstone conference held at the United States Military Academy in West Point, NY. Following careful review of 48 academic paper submissions, eight were selected for publication in this journal. Each paper incorporated features of systems or industrial engineering and presented detailed and reflective analysis in the topic. Three general bodies of knowledge in the papers include: systems engineering and decision analysis, modeling and simulation, and artificial intelligence Systems Engineering and Decision Analysis topics included three unique contributions. The work of Flanick et al. examined adaptability in Hyper-Enabled Operator systems and recommended how each technology might address capability gaps for special operations forces. Wilby et al. employed a scalable predictive statistical model for decision support to significant work package prioritization for U.S. Army Corps of Engineers nationally significant inland waterway infrastructure. Contributions by Shi et al. employed value focused thinking and a robust cost model to enable decision quality for PM Cargo CH-47 technologies. Modeling and Simulation works also included three unique contributions. Recognized as ‘best paper’ at the 2019 conference, work by Cooley et al. developed a senior leader engagement model using sparse K-means clustering techniques to greatly improve the planning and execution for AFRICOM leadership. Lovell et al. employed robust military simulation models to evaluate and propose solutions Soldier Search and Target Acquisition protocols. Work by Drake et al. employed vehicle Routing Problem simulation software to enhance United Health Services material handling challenges across NY State thus enabling quality optimization choices. Finally, two unique contributions in artificial intelligence examined key text mining technologies. Shi et al. employed text mining and Latent Dirichlet Allocation modeling to derive insights through trends and clustering narratives on U.S. Army Officer Evaluation Reports and describe success. Similarly, text mining techniques by Senft et al. helped to examine and show similarities in success narratives across genders thus providing valuable insights for promotion boards. Congratulation to the 2019 undergraduate scholars and all authors who provided valuable contributions through thoughtful and steadfast intellectual efforts to their fields of study! LTC James H. Schreiner, PhD, PMP, CPEM Director, Operations Research Center Department of Systems Engineering United States Military Academy Mahan Hall, Bldg 752, Room 305 West Point, NY 10996, USA [email protected]

scholarly journals Predicting and Responding to Clinical Deterioration in Hospitalized Patients by Using Artificial Intelligence: Protocol for a Mixed Methods, Stepped Wedge Study

10.2196/27532 ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. e27532
Author(s):  
Laura M Holdsworth ◽  
Samantha M R Kling ◽  
Margaret Smith ◽  
Nadia Safaeinili ◽  
Lisa Shieh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods ◽  
Clinical Outcomes ◽  
Systems Engineering ◽  
Multidisciplinary Team ◽  
Hospital Medicine ◽  
Clinical Deterioration ◽  
Emergent Properties ◽  
Stepped Wedge ◽  
Work System

Background The early identification of clinical deterioration in patients in hospital units can decrease mortality rates and improve other patient outcomes; yet, this remains a challenge in busy hospital settings. Artificial intelligence (AI), in the form of predictive models, is increasingly being explored for its potential to assist clinicians in predicting clinical deterioration. Objective Using the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model, this study aims to assess whether an AI-enabled work system improves clinical outcomes, describe how the clinical deterioration index (CDI) predictive model and associated work processes are implemented, and define the emergent properties of the AI-enabled work system that mediate the observed clinical outcomes. Methods This study will use a mixed methods approach that is informed by the SEIPS 2.0 model to assess both processes and outcomes and focus on how physician-nurse clinical teams are affected by the presence of AI. The intervention will be implemented in hospital medicine units based on a modified stepped wedge design featuring three stages over 11 months—stage 0 represents a baseline period 10 months before the implementation of the intervention; stage 1 introduces the CDI predictions to physicians only and triggers a physician-driven workflow; and stage 2 introduces the CDI predictions to the multidisciplinary team, which includes physicians and nurses, and triggers a nurse-driven workflow. Quantitative data will be collected from the electronic health record for the clinical processes and outcomes. Interviews will be conducted with members of the multidisciplinary team to understand how the intervention changes the existing work system and processes. The SEIPS 2.0 model will provide an analytic framework for a mixed methods analysis. Results A pilot period for the study began in December 2020, and the results are expected in mid-2022. Conclusions This protocol paper proposes an approach to evaluation that recognizes the importance of assessing both processes and outcomes to understand how a multifaceted AI-enabled intervention affects the complex team-based work of identifying and managing clinical deterioration. International Registered Report Identifier (IRRID) PRR1-10.2196/27532

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