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ردیف | عنوان | نوع |
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1 |
Evaluation of corporate requirements for smart manufacturing systems using predictive analytics
ارزیابی الزامات شرکت برای سیستمهای تولید هوشمند با استفاده از تجزیه و تحلیل پیشبینیکننده-2022 Smart manufacturing systems (SMS) are one of the most important applications in the Industry
4.0 era, offering numerous advantages over traditional production systems and rapidly being
used as a performance-enhancing strategy of manufacturing enterprises. A few of the technologies that must be connected to construct an SMS are the Industrial Internet of Things (IIoT),
Big Data, Robotics, Blockchain, 5G Communication, Artificial Intelligence (AI), and many more.
SMS is an innovative and popular manufacturing setup that produces increasingly intelligent
production systems; yet, designers must adapt to business tastes and requirements. This study
employs an analytical and descriptive research technique to identify and assess functional and
non-functional, technological, economic, social, and performance evaluation components that
are essential to SMS evaluation. A predictive analytics framework, which is a key component
of many decision support systems, is used to assess corporate needs as well as proposed and
prioritize SMS services.
keywords: صنعت 4.0 | تجزیه و تحلیل پیش بینی کننده | سیستم های تولید هوشمند | اینترنت اشیاء صنعتی | سیستم پشتیبانی تصمیم | Industry4.0 | Predictive analytics | Smart manufacturing systems | Industrial Internet of Things | Decision support system |
مقاله انگلیسی |
2 |
An easy-to-explain decision support framework for forensic analysis of dynamic signatures
یک چارچوب پشتیبانی تصمیم آسان برای تجزیه و تحلیل پزشکی قانونی امضاهای پویا-2021 Forensic handwriting examination is often criticized for its lack of objective standards and rigorous scientific validation. On the other hand, cutting-edge techniques for biometric handwriting and signature verification are often perceived as perfect black boxes and are not used by forensic handwriting examiners in their work environment. This paper presents an easy-to-explain yet effective framework to support semi-automatic signature verification in forensic settings. The proposed approach is based on measuring similarities between signatures by applying Dynamic Time Warping on easy-to-derive dynamic features. The goal is to provide forensic handwriting examiners with a decision support tool for making reproducible and less questionable inferences, while being both intuitive and easy to explain. The method is tested on a newly proposed dataset that also takes into account the so-called disguised sig- natures which are of extreme importance in this scenario.© 2021 Elsevier Ltd. All rights reserved. Keywords: Dynamic signatures | Forensic handwriting examination | Behavioral biometrics | Decision support system | Disguised signatures |
مقاله انگلیسی |
3 |
Capturing causality and bias in human action recognition
ثبت علیت و سوگیری در تشخیص عمل انسان-2021 Human action recognition using various sensors is a mandatory component of autonomous vehicles, humanoid robots, and ambient living environments. A particular interest is the detection and recognition of falls. In this paper, we propose the use of temporal convolution networks guided by knowledge distilla- tion for detecting falls and recognizing types of falls using accelerometer data. Tri-axial accelerometers attached to the body measure the acceleration of the body joints when an action occurs. These data are used for pattern analysis and body action recognition. We demonstrate the existence of biases caused by soft biometrics when recognizing human body actions. We introduce a causal network to capture the influences of biases on system performance and illustrate how knowledge distillation can be applied to mitigate the bias effect. Crown Copyright © 2021 Published by Elsevier B.V. All rights reserved. Keywords: Machine learning | Decision support | Human action recognition | Machine reasoning | Belief networks |
مقاله انگلیسی |
4 |
Transformation of semantic knowledge into simulation-based decision support
تحول دانش معنایی به پشتیبانی تصمیم گیری مبتنی بر شبیه سازی-2021 Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, indepth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model.
This knowledge-intensity leads to long development times of respective projects, which contradicts the need
for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to
foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation
model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides
a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully
parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing
guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into
instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated
transformation of this conceptual model into an executable one. This novel modeling approach makes model
development more efficient by reducing its complexity. It is validated in a use case implementation from
semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate
the impacts of the COVID-19 pandemic on a global supply chain network.
keywords: تحول دانش | پشتیبانی تصمیم | هستی شناسی | مدل سازی ترکیبی | شبیه سازی همه گیر | شبیه سازی زنجیره تامین | Knowledge Transformation | Decision Support | Ontologies | Hybrid modeling | Pandemic Simulation | Supply chain simulation |
مقاله انگلیسی |
5 |
A decision support tool for cement industry to select energy efficiency measures
یک ابزار پشتیبانی تصمیم گیری برای صنعت سیمان برای انتخاب اقدامات بهره وری انرژی-2020 Cement industry is one of the most energy intensive industrial sub-sectors. It accounts for almost 15% of the total
energy consumed by manufacturing. Numerous energy efficiency initiatives and measures have been introduced
and employed in this industry. To implement the most appropriate solutions for a certain cement plant, both
technological and non-technological constraints need to be considered. To date, researchers have focused on
outcomes such as energy savings, investment and emission reduction and therefore, both qualitative criteria and
current circumstances of the plant have been largely overlooked. In this study, an integrated 3-phase model is
presented to address these shortcomings and assist the plant managers to select and invest in the most suitable
projects. The proposed tool, which is founded on a multi-criteria decision model, will assist the cement managers
in achieving their energy saving targets. The tool is tested for 3 cases showing its applicability with real data
resulting in the ranked list of opportunities for each of the plants. Keywords: Cement industry | Energy efficiency | Decision support tool | Energy management |
مقاله انگلیسی |
6 |
Telemedicine DSS-AI Multi Level Platform for Monoclonal Gammopathy Assistance
پلت فرم چند سطحه از راه دور پزشکی DSS-AI برای کمک به گاموپاتی مونوکلونال-2020 The proposed work describes preliminary results
of a research project based on the realization of a Decision
Support System -DSS- platform embedding medical and
artificial intelligence -AI- algorithms. Specifically the
telemedicine platform is suitable for the optimization of
assistance processes of patients affected by Monoclonal
Gammopaty. The results are related to the whole design of the
platform implementing a DSS based on a multi-level decision
making process. Starting from the main architecture
specifications, is formulated a flowchart based on different
alerting levels of patient risk including artificial intelligence -
AI- decision supporting facilities. Finally, the perspectives of
the performed research are discussed. Keywords: Telemedicine | Digital Assistance | Decision Support System | Artificial Intelligence | Monoclonal Gammopathy |
مقاله انگلیسی |
7 |
The Importance of the Social License to Operate at the Investment and Operations Stage of Coal Mining Projects: Application using a Decision Support System
اهمیت پروانه اجتماعی برای فعالیت در مرحله سرمایه گذاری و عملیات پروژه های استخراج زغال سنگ: درخواست با استفاده از سیستم پشتیبانی تصمیم-2020 The Social License to Operate (SLO) and the Value Chain business model are basic elements that need to be considered both at the planning and operation stages of mining operations and in particular in coal mining projects. If a coal mining enterprise loses its SLO, it may face risks in operations, which may lead to value chain risks. One of the causes of enterprise failure as related to coal mining operations is the inability to reliably assess/ manage risk holistically and the inability to understand that lack of SLO is a critical risk. Although financial risks are typically assessed for mining projects, lack of SLO risk should also be taken into account starting as early as the bankable feasibility study. Furthermore, as it is difficult to establish a proactive decision-making policy for SLO risk in coal mining operations, the Operational Risk Management (ORM) methodology is probably a good tool to apply towards that goal. For this reason, a Mining Operational Risk Management Model (MORMM) was developed to incorporate risk probabilities and risk severities evaluated by experts. The final risk assessment is coded using Risk Assessment Codes (RACs). A hypothetical scenario was developed utilizing the MORMM model in order to illustrate how risks can be managed during the SLO granting process. This scenario describes a hypothetical coal mining project evaluated by virtual risk evaluators under specific hazard categories. Risk evaluation involves the assessment of risk probability and risk severity. Through this scenario this paper presents ways: (i) to establish a baseline ORM process that will be applicable to any coal mining operation environment, and (ii) to provide a theoretical example to demonstrate how the method can be applied to coal mining operations. The resulting RACs can provide critical information to decision makers regarding the rejection, acceptance or re-engineering of the mining business plan. Keywords: Social License to Operate | Operational risk management | coal mining | Value Chain |
مقاله انگلیسی |
8 |
Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI
اعتماد به نفس و پذیرش مبتنی بر هوش مصنوعی ایجاد کنید سیستم های پشتیبانی تصمیم - هوش مصنوعی قابل توضیح و مسئولیت پذیر-2020 Artificial Intelligence has known an incredible
development since 2012. It was due to the impressive
improvement of sensors, data quality and quantity, storage and
computing capacity, etc. The promises AI offered led many
scientific domains to implement AI-based decision support tool.
However, despite numerous amazing results, very serious
failures have raised Human mistrust, fear and scorn against AI.
In Industries, staff members cannot afford to use tools that
might fail them. This is especially true for Transportation
operators where security and safety are at risk. Then, the
question that arises is how to build Human confidence and
acceptance of AI-based decision support system. In this paper,
we combine different points of view to propose a structured
overview of Transparency, Explicability and Interpretability,
with new definitions arising as a consequence. Then we discuss
the need for understandable information from the AI system, to
legitimate or refute the tool’s proposal. To conclude we offer
ethical reflexions and ideas to develop confidence in AI. Keywords: explainable AI | liable AI | decision support system | confidence | technology |
مقاله انگلیسی |
9 |
An analytic infrastructure for harvesting big data to enhance supply chain performance
یک زیرساخت تحلیلی برای برداشت داده های بزرگ به منظور افزایش عملکرد زنجیره تأمین-2020 Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this pa- per, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infras- tructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based opera- tional decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm’s op- erations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development. Keywords: Decision support systems | Big data | Analytic infrastructure | Competence set | Deduction graph |
مقاله انگلیسی |
10 |
A decision support system using hybrid AI based on multi-image quality model and its application in color design
یک سیستم پشتیبانی تصمیم گیری با استفاده از هوش مصنوعی ترکیبی مبتنی بر مدل کیفیت چند تصویر و کاربرد آن در طراحی رنگ-2020 The product-color image conveys consumers’ color demands through emotion cognition. In this paper,
a decision support system is proposed based on the hybrid artificial intelligence algorithm. The
proposed system explores the internal correlation between the color image and demand of users.
In the proposed system, an artificial neural network based on the radial basis function is employed.
The network model is trained with an improved particle swarm optimization combined with the
weight-adaptive strategy and chaos theory. The proposed model predicts the multi-uses’ color images.
Then, the decision colors are extracted from the predicted colors by K-harmonic means clustering. The
experimental results show that the proposed color decision support system is promising in designing
the color scheme and providing theoretical guidance for the product-color design. Keywords: Decision support system | Artificial intelligence | Product design | Multi-users’ images |
مقاله انگلیسی |