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Partially Defined Logical Operators in Cause-Effect Models 24th

Partially Defined Logical Operators in Cause-Effect Models

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24th International TRIZ Future Conference

作者： Chrzaszcz, Jerzy Warsaw Univ Technol Inst Comp Sci PL-00665 Warsaw Poland

ISBN: (纸本)9783031759253;9783031759239;9783031759222

Cause-effect models used in TRIZ to identify key disadvantages of the analyzed systems employ AND and (explicit or implicit) OR operators to indicate how the causes trigger the effects. These fully defined logical operators imply disadvantage elimination strategies, i.e., removing any of the AND-connected causes vs. removing all OR-connected causes. On the contrary, the partially defined logical operators incur uncertainty about the trigger conditions due to the output values being undefined for some input combinations. This paper proposes a method of handling such operators to support decisions regarding disadvantage elimination, which may interest TRIZ practitioners and researchers. The paper starts with recalling the basics of cause-effect analysis and introducing the notations for describing Boolean functions. The requirements and sample statistics concerning disadvantage descriptions are discussed in the second section, while the third section introduces partially defined operators. The following two sections present the proposed categorization of logical functions and the method for systematic selection of contributing causes to remove. The summary and ideas for further research are given in the last section of the paper.

关键词： TRIZ cause-effect analysis logical operators partially defined functions Boolean logic

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User Behavior Forensics on Encrypted Traffic in the Industrial Internet of Things 20th

User Behavior Forensics on Encrypted Traffic in the Industri...

引用

20th ifip WG 11.9 International Conference on Digital Forensics

作者： Zhu, Zhishen Gou, Gaopeng Wang, Chonghua Zhou, Hao Lin, Chen Xiong, Gang Chinese Acad Sci Beijing Peoples R China China Ind Control Syst Cyber Emergency Response T Beijing Peoples R China

ISBN: (纸本)9783031710278;9783031710254;9783031710247

The Industrial Internet of Things is an emerging technology that has rapidly penetrated diverse applications. MindSphere from Siemens stands out as a leader among Industrial Internet of Things solutions. Unlike most industrial control systems, MindSphere utilizes robust encryption protocols such as SSL, TLS and even QUIC for data transmission, presenting significant challenges to traditional traffic forensic approaches. Conducting effective user behavior forensics in this context requires the identification of relevant traffic in extensive IP network flows along with the execution of fine-grained classification tasks. The challenges significantly reduce the effectiveness of mainstream encrypted traffic classification methods on MindSphere. This chapter describes a novel flow-correlation framework that is designed to automatically extract user behavior patterns from MindSphere network traffic. The approach engages a hybrid feature set encompassing statistical and sequential data to create feature vectors for flow nodes. By leveraging traffic correlation, the approach incorporates a burst time-domain mechanism to construct a communications diagram. The constituent traffic graphs are processed using a graph neural network model to enable effective classification. Comprehensive experiments demonstrate that the approach exhibits outstanding performance that surpasses state-of-the-art methods.

关键词： Industrial Internet of Things Encrypted Traffic Classification User Behavior Forensics Graph Neural Network Model

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Predictive Data Analysis Platform, for Optimizing and Automating the Distribution of Car Insurance Products, Based on Telematic Data 24th

Predictive Data Analysis Platform, for Optimizing and Automa...

引用

24th International TRIZ Future Conference

作者： Barna, Erik Barcau, Emanuel Ravaru, Raul Life Is Hard Software Co Floresti Romania

ISBN: (纸本)9783031759253;9783031759239;9783031759222

The purpose of this article is to present an AI technology based innovative approach, involved in a platform for digitizing processes in the Car Insurance Business field, which allows the end user (the broker of insurance or the insurer) to base his decisions on robust information to help him make a robust business forecast. Predictive analytics for insurance entails the use of special technology to sift through and analyze historical telematics data and consumer trends in effort to project future behavior. Obviously combining the AI based IT technologies with mathematical and statistical models, the integrated digitalized platform presented in this article involve also both Data Modeling and Deep Learning. Practically, the software platform presented in this article represents the backbone of any insurance brokerage business, because without such an application it is impossible to manage business processes that have hundreds or even thousands of sales agents. From structural point of view, this platform has a layered structure, the first layer being the basic brokerage application, this being extended with innovative predictive computational modules as upper layers. The technical implications mainly refers to the innovative way of involving in the digitalized system a massive amount of telematics data. The expected business implications consists in offering, by an innovative digitalized solution, the possibility for the final client (insurance broker or insurer) to receive information that will help him make a forecast of business.

关键词： Computational Models Computational Science and Engineering Telematics Data Mining Clustering Methods

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AI-Aided Resource Mining Method for Idealization-Driven Product Innovation 24th

AI-Aided Resource Mining Method for Idealization-Driven Prod...

引用

24th International TRIZ Future Conference

作者： Du, Ranye Sun, Jianguang Miao, Runze Zhang, Delong Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China Hebei Univ Technol Natl Engn Res Ctr Technol Innovat Method & Tool Tianjin 300401 Peoples R China

ISBN: (纸本)9783031759215;9783031759192;9783031759185

The availability of resources in design plays a crucial role in innovative design. The importance of available resources increases as the solution to the problem approaches the Ideal Final Result (IFR). This paper introduces a method formining resources in the product innovation process by integrating the Theory of Inventive Problem Solving (TRIZ) and AI, and demonstrates TRIZ's application in various fields. Firstly, through AI tools to obtain research hotspots to formulate product design goals, through the training of ChatGPT, with the help of AI tools to achieve the mining of internal resources. Additionally, an AI algorithm is employed to analyze patents and identify external available resources. We evaluate the value of these mined resources using a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation model that combines analytic hierarchy process with entropy weight method to select optimal available resources. Multi-criteria evaluation methods and AI technology are applied to resource mining and selection for product innovation.

关键词： AI and Triz Topsis AI-assisted Resource Mining

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Standardization of Engineering and Systemic Innovation 24th

Standardization of Engineering and Systemic Innovation

引用

24th International TRIZ Future Conference

作者： Senghor, Ousmane Ndiaye, Marie Gaye, Khalifa LI3 Univ Assane Seck Ziguinchor BP 523 Ziguinchor Senegal

ISBN: (纸本)9783031759253;9783031759239;9783031759222

This article offers a methodological contribution for the standardization of engineering and systemic innovation with a view to supporting design teams in the development of eco-innovative products. We propose a unified methodology that will allow users to self-guide to create eco-innovative product concepts through the application of C-K theory combined with the TRIZ method while integrating the principles of the ASIT method. Firstly, understanding the links connecting the different sources of knowledge linked to the birth of eco-innovative concepts makes it possible to design one or more generic solutions. A simplified and adapted application of TRIZ will lead us to an eco-innovative solution. Thus, to refine the results, an AI algorithm will generate new solutions from the data and conditions recorded within the artificial intelligence.

关键词： Ecodesign Ecoinnovation TRIZ ASIT CK AI Ontology

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Innovative Approach to Autonomous Lavender Harvesting Robot Design Through Cognitive and Systematic Design Integration 24th

Innovative Approach to Autonomous Lavender Harvesting Robot ...

引用

24th International TRIZ Future Conference

作者： Balog, Bogdan Brad, Stelian Tech Univ Cluj Napoca Cluj Napoca 400114 Romania

ISBN: (纸本)9783031759253;9783031759239;9783031759222

Addressing the intricate challenges of lavender harvesting, this study pioneers the development of an autonomous robot, integrating cutting-edge methodologies to enhance agricultural robotics. Traditional harvesting methods have faltered in efficiently collecting lavender, a crop demanding precise and gentle handling. By synthesizing the "Cognitive Breakthrough Assistant" (COBRA), generative AI, the Theory of Inventive Problem Solving (TRIZ), and the Complex Systems Design Technique (CSDT), our research seeks to dismantle psychological inertia, fostering a fertile ground for innovative solutions in robotic design. The methodology adopts COBRA to pinpoint and disrupt entrenched thinking patterns, utilizing its natural language processing to reassess and redefine problem-solving paradigms. The amalgamation of generative AI with TRIZ and CSDT propels forward the ideation and design phases, crafting a robot equipped with sophisticated navigation, delicate handling mechanisms, and adaptive algorithms tailored for the nuances of lavender fields. This interdisciplinary approach yields a prototype that excels in diverse harvesting scenarios and sets the stage for its optimization toward commercial viability, emphasizing scalability and sustainability. With rigorous field trials, the prototype's efficacy in real-world conditions promises a significant leap toward commercial production.

关键词： Autonomous Lavender Harvesting Robot COBRA CSDT TRIZ Generative AI

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Topological Optimization of a Car Module with TRIZ and Machine Learning 24th

Topological Optimization of a Car Module with TRIZ and Machi...

引用

24th International TRIZ Future Conference

作者： Brad, Stelian Rat, Dana Ioana Tech Univ Cluj Napoca Cluj Napoca 400114 Romania ARRK Engn R&D Cluj Cluj Napoca 400394 Romania

ISBN: (纸本)9783031759253;9783031759239;9783031759222

This study explores a methodology for the topological optimization of car modules by integrating TRIZ (Theory of Inventive Problem Solving) and machine learning techniques. Initially, TRIZ principles guide the qualitative optimization phase, establishing proper design directions aimed at weight reduction and durability enhancement. Following this, machine learning tools, including ARRK's proprietary algorithms, are applied for precise parametric optimization, ensuring alignment with performance criteria. The findings demonstrate the efficacy of this integrated approach, significantly improving car module design by refining geometrical proportions and achieving dual objectives: weight reduction and enhanced strength. While the study highlights the potential of combining TRIZ and machine learning, it acknowledges limitations due to the use of freely available 3D models and the proprietary nature of certain algorithms. Nonetheless, this research provides a comprehensive framework for automotive engineers and designers, setting a new benchmark for incorporating qualitative insights into the quantitative optimization of complex systems.

关键词： Topological Optimization Qualitative Optimization Quantitative Optimization TRIZ Machine Learning Parametric Optimization CarModule

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Optimizing Green Hybrid Energy Systems Through Cognitive Innovator Xcelerator (CIX) 24th

Optimizing Green Hybrid Energy Systems Through Cognitive Inn...

引用

24th International TRIZ Future Conference

作者： Brad, Stelian Balan, Daniel Tech Univ Cluj Napoca Cluj Napoca 400114 Romania

ISBN: (纸本)9783031759253;9783031759239;9783031759222

The pressing need for sustainable and efficient energy solutions has driven the exploration of hybrid systems combining biomass, photovoltaic, and other renewable sources. This study introduces the Cognitive Innovator Xcelerator (CIX), an advanced AI framework designed to optimize the design and integration of these complex systems. CIX employs a multi-faceted approach, integrating Intelligent Problem Mapping and ASIT Thinking Modules to analyze and address challenges in hybrid energy system design. By leveraging AI-driven idea generation and solution refinement, CIX facilitates a systematic exploration of potential design configurations. The application of CIX led to the identification of novel, efficient configurations for hybrid energy systems, surpassing conventional designs in energy output and sustainability metrics. Physical testing of the optimized design confirmed theoretical predictions, demonstrating significant improvements in energy efficiency and system integration. Through intelligent problem mapping and creative solution generation, CIX proves to be a powerful tool in advancing the development of sustainable hybrid energy systems.

关键词： Hybrid Energy System Cognitive Innovator Xcelerator ASIT Thinking Modules AI-Driven Idea Generation Biomass Photovoltaic Energy Optimization Sustainable Energy Solutions

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Dynamic IoT Determination of Overall Heat Transfer Coefficient in a Portable Cabin in Kuwait 7th

Dynamic IoT Determination of Overall Heat Transfer Coefficie...

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7th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： Sedaghat, Ahmad Nazififard, Mohammad Al-Khiami, Mohamad Iyad Australian Univ Kuwait Dept Mech Engn Coll Engn Safat 13015 West Mishref Kuwait Univ Cote Azur Polytech Lab Nice France Australian Univ Kuwait Coll Engn Dept Civil Engn Safat 13015 West Mishref Kuwait

ISBN: (纸本)9783031819025;9783031819001;9783031818998

Two portable cabins (size 2 m width, 2 m depth, 2.8 m height) were constructed using 75mm Polyurethane sandwich panels (insulation walls) to simulate low energy buildings in Kuwait. Experimental measurements were conducted using eight K-type thermocouple probes on interior/exterior surfaces of the cabins' walls to dynamically evaluate overall heat transfer coefficient during the hot months of August and September 2023 using IoT data storage. The collected data was further curated to ensure quality by treating negative values, handling potential division by zero issues, and filtering out large numbers. The dynamic average overall heat transfer coefficients were found to be 0.186, 0.198, 0.203, and 0.206 W/m(2)K for the East, North, West, and South facing walls, respectively. This is far better than the static (steady state) overall heat transfer coefficient of 0.2534W/m(2)K, indicating that the dynamic behavior of the insulation walls is very promising, particularly by saving 21.76% cooling energy demands in summertime in Kuwait.

关键词： Insulation Sandwich Panels Kuwait's Hot Climate Low-Energy Building

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A Novel Interdisciplinarity Model Towards Inter-domain information Pairing 24th

A Novel Interdisciplinarity Model Towards Inter-domain Infor...

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24th International TRIZ Future Conference

作者： Douard, Nicolas Samet, Ahmed Giakos, George Cavallucci, Denis Univ Strasbourg Inst Natl Sci Appl 24 Bd Victoire F-67000 Strasbourg France Manhattan Coll Dept Elect & Comp Engn 3825 Corlear Ave New York NY 10463 USA

ISBN: (纸本)9783031759253;9783031759239;9783031759222

This study introduces an interdisciplinary prediction framework as part of a novel approach that integrates the Inventive Design Method (IDM), Topic Modeling, and Generative AI to foster innovation across academic fields. Identifying interdisciplinary connections is essential for solving complex, multi-domain problems. Our research uses a supervised machine learning classifier to identify interdisciplinary documents within the Semantic Scholar corpus, extracting latent insights. The Text Convolutional Neural Network model performed best, achieving an F1 score of 0.80. We find that approximately 25% of human knowledge is interdisciplinary. This framework helps create comprehensive knowledge maps across multiple domains, promoting innovation through effective cross-domain knowledge transfer.

关键词： Interdisciplinary mapping cross-domain transfer biomimicry literature analysis topic models language models

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