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Discovering Instance Spanning Exceptions from process Execution Logs 24

Discovering Instance Spanning Exceptions from Process Execut...

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24th IEEE International conference on Business Informatics (CBI)

作者： Stertz, Florian Winter, Karolin Rinderle-Ma, Stefanie Univ Vienna Fac Comp Sci Vienna Austria Tech Univ Munich Dept Informat Munich Germany

ISBN: (数字)9781665460163

ISBN: (纸本)9781665460163

Exceptions in process execution occur frequently and require appropriate handling strategies in order to avoid undesired consequences. For quality control in manufacturing processes, for example, when a blade gets missing, the set of affected instances is put on hold until the blade is found. As it can be seen from this example, the exception affects multiple instances and is hence denoted as instance spanning exception. Exceptions leave footprints in the process execution logs of the affected instances. Hence, process execution logs provide a valuable data source for discovering and analyzing exceptions. However, the discovery of instance spanning exceptions is still an open challenge. Thus, this paper proposes i) a classification of instance spanning exceptions based on literature and a set of real-world examples, followed by ii) a description of how instance spanning exceptions manifest in process execution logs along with an elicitation of minimal requirements for enabling their discovery, and iii) five instance spanning exception discovery algorithms, one for each class. The discovery algorithms are implemented and evaluated on a set of synthetic process execution logs reflecting real-world instance spanning exceptions and on a real-world process execution log from the public transport domain demonstrating the feasibility as well as applicability of the presented algorithms.

关键词： Instance Spanning Exceptions Exception Discovery and Handling process analysis and Improvement process Aware Information Systems

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4th International conference on Intelligent Systems in Production Engineering and Maintenance , ISPEM 2023

4th International Conference on Intelligent Systems in Produ...

引用

4th International conference on Intelligent Systems in Production Engineering and Maintenance , ISPEM 2023

ISBN: (纸本)9783031450204

The proceedings contain 44 papers. The special focus in this conference is on Intelligent Systems in Production Engineering and Maintenance. The topics include: Information Design Management of Machining Parts on Metal Cutting Machines;Virtual Training at ELLA Platform – The Case Study;web Application for the Learning of Emotions in Children with Autism Spectrum Disorder;lean Manufacturing Knowledge Correlation Model;assessment of the Effectiveness of Six Sigma Methodology Implementation - A Literature Review;analysis of Basic Characteristics of Textile Yarn Using Image processing Techniques;modeling the Impact of Leadership on Change Management Decisions Using System Dynamics Simulation;Using an ISO 9001 Based Framework as a Benchmark to Identify Best Practices Used by Sri Lankan Practitioners when Selecting Suppliers;requirements for Flow control Systems for Discrete Production of Mechanical Products;NIRF Imaging with Indocyanine Green (ICG) in a Veterinary Minimally Invasive Surgery;an Augmented System Based on Machine Learning for Boccia Assisted Gameplay;Ultrasound Tissue Differentiation (UTC) an Objective Diagnostic Tool in Prevention and Healing Monitoring Tendon Problems in Race Horses;location of Obstacles on Transport Routes in a Centralized Multi-agent System;converging data Mesh and Microservice Principles into a Unified Logical Architecture;Development and Comparative analysis of IT Tools Supporting data processing in Custom Manufacturing of Orthoses;design of Experiments: An Overview and Future Paths;SOC Evaluation of Lithium-ion Battery Based on MFC Sensors;indicators of Hierarchical Structure Model of Supporting the Production Management process – A Framework;gamification-Based Crowdsourcing as a Tool for New Product Development in Manufacturing Companies.

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Generalized data Distribution Iteration 39

Generalized Data Distribution Iteration

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39th International conference on Machine Learning (ICML)

作者： Fan, Jiajun Xiao, Changnan Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China ByteDance Beijing Peoples R China

To obtain higher sample efficiency and superior final performance simultaneously has been one of the major challenges for deep reinforcement learning (DRL). Previous work could handle one of these challenges but typically failed to address them concurrently. In this paper, we try to tackle these two challenges simultaneously. To achieve this, we firstly decouple these challenges into two classic RL problems: data richness and exploration-exploitation trade-off. Then, we cast these two problems into the training data distribution optimization problem, namely to obtain desired training data within limited interactions, and address them concurrently via i) explicit modeling and control of the capacity and diversity of behavior policy and ii) more fine-grained and adaptive control of selective/sampling distribution of the behavior policy using a monotonic data distribution optimization. Finally, we integrate this process into Generalized Policy Iteration (GPI) and obtain a more general framework called Generalized data Distribution Iteration (GDI). We use the GDI framework to introduce operator-based versions of well-known RL methods from DQN to Agent57. Theoretical guarantee of the superiority of GDI compared with GPI is concluded. We also demonstrate our state-of-the-art (SOTA) performance on Arcade Learning Environment (ALE), wherein our algorithm has achieved 9620.33% mean human normalized score (HNS), 1146.39% median HNS and surpassed 22 human world records using only 200M training frames. Our performance is comparable to Agent57's while we consume 500 times less data. We argue that there is still a long way to go before obtaining real superhuman agents in ALE.

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Probabilistic analysis of GPS Spoofing Attack Failures Using Poisson Point process

Probabilistic Analysis of GPS Spoofing Attack Failures Using...

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Power, control and Computing Technologies (ICPC2T), conference on

作者： D. Kiruthika N. Vinothini G. Ananthi Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai India

This study presents a geospatial analysis approach to derive the failure probability of GPS spoofing attacks by utilizing Poisson Point process (PPP) modeling. The research begins by establishing a robust framework for characterizing GPS spoofing attacks based on available data and historical attack patterns. Leveraging the PPP model, which is commonly used in spatial point process analysis, the study integrates real-world information such as GPS receiver locations, environmental conditions, and spoofing device parameters to simulate the spatial distribution of spoofing attack attempts across a given area. Through the application of PPP modeling, the study aims to estimate the failure probability of GPS spoofing attacks, which is defined as the likelihood of the spoofer's signal being successfully received and accepted by a targeted GPS receiver. This approach allows for the incorporation of various geospatial factors, including receiver density, signal propagation, and environmental interference, to provide a more accurate representation of the vulnerability of the GPS system to spoofing attacks in specific geographic areas. The findings from this research are expected to contribute to enhancing the understanding of GPS vulnerability and improving countermeasures against spoofing attacks. The closed form expression for GPS Spoofing attack failure probability is derived using the Poisson point process. The simulation is compared with the theoretical results and observed that it is exactly close to each other.

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Discussion of Sewage Sludge Co-incineration based on Dioxin Emission Numerical Simulation Model for Municipal Solid Waste Incineration process

Discussion of Sewage Sludge Co-incineration based on Dioxin ...

引用

Chinese control and Decision conference, CCDC

作者： Wenbo Ma Jian Tang Heng Xia Jiakun Chen Tianzheng Wang Yongqi Liang Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Co-incineration is one of the best ways to treat sewage sludge (SS) and municipal solid waste (MSW), but it is very important to understand its combustion and gas reaction in the grate to control the emission of pollutants. In this paper, the discussion of SS co-incineration based on the dioxin emission numerical simulation model for the MSW incineration (MSWI) process is proposed. Firstly, the numerical simulation model of DXN emission for the MSWI process is established in ASPEN Plus software by using collected sample data in an MSWI plant in Beijing. Then, the SS and MSW co-incineration numerical simulation model is constructed. Finally, based on SS data in reference, the discussion and analysis are made. These research results provide support for the future research on co-incineration of SS and MSW in Beijing.

关键词： Waste management Employee welfare Waste materials Solid modeling Incineration process control Numerical simulation

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CART-ii: Development of Collision Avoidance Robotic Tether with Soft Sensing Capabilities for Underwater Nuclear Inspection Vehicles 9

CART-II: Development of Collision Avoidance Robotic Tether w...

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9th International conference on Automation, Robotics and Applications (ICARA)

作者： Al Mhdawi, Ammar K. Wright, Nick Benson, Simon Haroutunian, Maryam Edge Hill Univ Dept Comp Sci & Engn Ormskirk England Newcastle Univ Dept EE Engn Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Dept Marine Engn Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781665489218

In nuclear inspection environments, a tether cable is used to transfer power and data between the underwater robotic system and the surface control unit. During underwater nuclear inspection, the tether cable can become entangled and loop with the environment such as nuclear waste boxes and objects. The risk of colliding with underwater objects is increased by the presence of more inspection robots underwater, especially if they are equipped with manipulator arms. As a result of the loops and knots around the cable, the inspection process may be affected and the ROV may not be able to perform its job. The present article is an extended development of the previous Collision Avoidance Robotic Tether (CART-I) model [1]. The CART-I system consists of micro thrusters that are attached to the base unit by a tether cable. The micro thrust unit is capable of generating a small amount of thrust that can move the tether away from obstacles in the water, particularly in restricted spaces. The use of light detection technologies such as IR or LiDAR for obstacle detection is not effective underwater due to the complex motion dynamics of the tether underwater and the size of obstacles, which makes it impossible to provide definite identification of the objects within a given time period. In order to provide the surroundings of the micro thrust units with obstacle detection capability, we have developed an autonomous force soft sensor. Additionally, the soft moulded sealed encase was developed for effective force detection underwater, and was experimentally tested in a water tank to validate our proposed design. Simulation and experimental results of the sensor is provided. The overall goal of the CART-ii is to provide a smart autonomous vision of obstacle avoidance using soft force sensing capabilities. This paper presents the full kinematic model and the simulation with finite element analysis of the CART-ii system with the hardware and physical implementation of the soft sensor

关键词： Tether force sensing conductive Remotely Operated Vehicles (ROV) marine environment thrust nuclear inspection 3D printing

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Research on Fault Diagnosis modeling Methods Driven by Route QAR data

Research on Fault Diagnosis Modeling Methods Driven by Route...

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International conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Yinlong Fang Feng Jin Nan Yang Lu Yang Guigang Zhang Jian Wang Beijing Institute of Technology Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Shanghai Aircraft Customer Service Co. Ltd. Shanghai China

ISBN: (数字)9798350388855

ISBN: (纸本)9798350388862

Diagnosing flight route faults based on QAR data is crucial for preventing flight accidents and reducing maintenance costs. This paper presents a method for diagnosing flight route faults using a Long Short-Term Memory Network and Decision tree algorithm (LSTM-DT). We used feature optimization algorithms to process the original QAR data, including rolling window operation and principal component analysis. The optimized features were then used to establish an LSTM anomaly detection model to identify abnormal points of the faulty route. We established a decision tree fault diagnosis model using these detected outliers to determine the corresponding fault types. When tested with real airline data, the fault diagnosis accuracy of this method reached 99.45%, confirming its effectiveness.

关键词： Fault diagnosis Analytical models Atmospheric modeling Feature extraction data models Decision trees Prognostics and health management Long short term memory Anomaly detection Principal component analysis

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Research on Causal Mechanisms and Visualization for Causal Inference Based on Drug Crimes 24

Research on Causal Mechanisms and Visualization for Causal I...

引用

Proceedings of the 2024 4th International conference on Computational modeling, Simulation and data analysis

作者： Shaobing Wu Key Laboratory of Intelligent Drug Control Institute of Information Security Yunnan Police College Kunming Yunnan China

ISBN: (纸本)9798400711831

Causal reasoning is a statistical paradigm that quantifies causal effects using observational data. This is a complex process that requires multiple steps, iterations, and collaboration with domain experts. Visualization is often relied upon to evaluate the accuracy of each step during analysis. However, existing visualization toolkits are not designed to support the entire causal inference process in the computing environment familiar to analysts. In this article, we use the Python visualization package Causalvis for causal reasoning to address the issue. On the basis of collecting relevant data on drug crimes, conduct causal relationship visualization and causal mechanism research on drug crimes.

关键词： Causal Mechanisms

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Chinese Painting Algorithm: A Study of Scene Characterization by Chromatographic Multiple analysis and Handwriting Co-construction

Chinese Painting Algorithm: A Study of Scene Characterizatio...

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IEEE/WIC/ACM International Joint conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Weng, Ziyang Yan, Weixin Hu, Yidan Weng, Zhimo Wuhan Univ Sch Informat Management Wuhan Peoples R China Shenzhen Museum Contemporary Art & Urban Planning Art Technol Lab Shenzhen Peoples R China Zhongnan Univ Econ & Law China Korea Inst New Media Wuhan Peoples R China

ISBN: (纸本)9781665494021

The understanding of scene representation is a deep knowledge service structure strategy arising from the increasing scale of data and the need for complex logic solving. This study proposes a modeling improvement method based on the fusion of complex feature data and exploration behavior trajectory extremes, which effectively utilizes the artistic feature study of the interplay between the unique colorant mixture attachment features of Chinese painting and complex handwriting features as the orientation region, realizes the classification constraint of colorant data through multispectral detection, and characterizes the handwriting as the behavior law, realizes the parametric extraction and then couples the solution encoding to complete the improvement of the algorithm. Since all scenes in Chinese painting are recorded in the bearer medium with handwriting characteristics after mixing Chinese brushes and colorants, the computational model of Chinese painting algorithm proposed in this paper starts from the processing of representation hierarchical structure and painting behavior of various scenes deposited to realize the principle of describing their material deposition goals and information exchange functions. The experimental analysis shows that i. deep knowledge understanding achieves the derivation of sparse feature validity, ii. the coverage calculation obtained by drawing on technological means can vividly describe the implicit characteristics of handwriting behavior, and iii. the improved modeling process has more humanized perceptual habits and enhances the accuracy and robustness of service domain requirements.

关键词： drawing multispectral reflectography Imaging spectroscopy Non-invasive diagnostics

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Research on Slope Stability analysis Based on Machine Learning 2

Research on Slope Stability Analysis Based on Machine Learni...

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2022 2nd International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2022

作者： Zhu, Quanpeng Jiangxi Teachers College Jiangxi Yingtan335000 China

ISBN: (纸本)9783800760268

As a hot artificial intelligence method, machine learning has penetrated into various industries. It has also been gradually introduced into the field of slope prevention and control, which has greatly promoted the development of this field. Therefore, this paper takes machine learning as the main research method, and uses the advantages of machine learning algorithm in regression and clustering to explore its application in slope stability analysis. This paper firstly analyzes the machine learning algorithm and its modeling process. Then the XGBoost algorithm is used for modeling and the genetic algorithm is used for model optimization. Based on the optimized model to evaluate the slope stability of Ningnan County, the results show that the optimized XGBoost evaluation model has the best effect, the evaluation accuracy rate is 89%, which is 1%~9% higher than other models, and the AUC value is 0.87, which is high. 0.05~0.13 for other models. And the time required is 29.26 seconds shorter than grid search, and the optimization efficiency is greatly improved. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Machine learning

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