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Warehouse Management and Performance Measurement- A Case Study for an Automotive Wiring Industry

Warehouse Management and Performance Measurement- A Case Stu...

引用

International conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Abdelaziz Mrabti Zeineb Boumallessa Sana Bouajaja Khaled Nouri Advanced System Laboratory (LSA) Carthage university LR11ES27 Polytechnic School of Tunisia Tunis Tunisia Laboratory of Electromechanical Systems (LASEM) National Engineering School of Sfax Sfax Tunisia Optimization and Analysis of Industrial and Service Systems Laboratory National Engineering School of Tunis Higher Institut of Industrial Management of Sfax Sfax Tunisia

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

Effective warehouse management and performance measurement are critical for enhancing operational efficiency and competitiveness in industries, particularly in the automotive sector where just-in-time manufacturing practices are prevalent. This case study explores the implementation of warehouse management strategies and performance measurement techniques within an automotive wiring industry context. By utilizing a visual management, the study investigates the challenges faced by the company in managing its warehouse operations and evaluates the impact of various performance metrics on overall productivity and profitability. After the warehouse management, the scrap rate was reduced from 0.98 g/m to 0.3 g/m. The findings highlight the importance of aligning warehouse management practices with organizational goals and leveraging key performance indicators to drive continuous improvement initiatives. Additionally, the case study identifies best practices and recommendations for optimizing warehouse operations and enhancing performance measurement frameworks in the automotive wiring industry, offering valuable insights for practitioners and researchers alike.

关键词： Industries Wiring Productivity visualization Profitability Key performance indicator Companies

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Explainable Machine Learning for Regime-Based Asset Allocation

Explainable Machine Learning for Regime-Based Asset Allocati...

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IEEE International conference on Big Data

作者： Ruoyun Zhang Chao Yi Yixin Chen Data Center China Asset Management Co. Ltd Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

This paper explores an explainable AI model in the financial industry. Macroeconomic and market data serve as inputs of Hierarchical Clustering to distinguish among different economic regimes. Compared with traditional models such as Investment Clock, this method can adjust the classification standard in time according to recent market sentiment. The regime, therefore, can be interpreted by not only macro indicators but also investors' mood swings using Artificial Intelligence. When we compute the statistical characteristics of returns of each asset, we find that they can be well distinguished among regimes. This method can also identify the abnormally large wave of the stock market from 2015 to 2016 by separating it as an unusual regime, which cannot be realized by traditional methods. The clustering technique enables us to explain and understand the current market status and predict different assets' performances. Therefore, thanks to the superior interpretability of AI, the mean and variance of returns in each regime are estimated and viewed as viewpoints of the Black-Litterman asset allocation model to construct portfolios. To simulate the real situation, a dynamic backtesting method is used and asset weights change because of the rolling time windows. The results show that equipped with a simple timing strategy, the clustering technique can improve the results and yield excess returns. Some other machine learning techniques are also applied in an attempt to improve the model.

关键词： Data visualization Macroeconomics Numerical models Resource management Stock markets optimization Portfolios

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Novel Stereo Visual Odometry Pipeline for Autonomous Robot Navigation on Video Data

Novel Stereo Visual Odometry Pipeline for Autonomous Robot N...

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Computing and Machine Intelligence (ICMI), International conference on

作者： Subhobrata Chakraborty Abhishek Verma Department of Computer Science California State Universitye Northridge CA USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Autonomous robot navigation relies on accurate motion estimation. Stereo Visual Odometry is used to estimate the motion of a camera by analyzing the visual information captured by a stereo pair of cameras. Visual odometry refers to the process of estimating the camera's movement by tracking the visual features in consecutive frames of a video sequence. In a stereo camera system, two cameras are positioned at known baseline separation. The images captured by the cameras are used to create a 3D representation of a scene. Stereo visual odometry leverages the disparity between corresponding points in the pair of stereo images to estimate the motion of camera. We propose a stereo visual odometry pipeline and perform a comprehensive comparison of different trajectory computation approaches on the challenging KITTI dataset. Different feature detection methods, feature matching, filter matching distance, stereo matching, different types of motion estimation, optimization techniques such as loop closure and bundle adjustment were tested in terms of error metric and computational time. SGBM matcher performed better than the BM matcher on depth and disparity estimation. SIFT was found to outperform the ORB feature detector. Brute Force feature matcher outperformed FLANN matcher. The lowest MAE error rate of 32.54 was obtained with SGBM matcher, brute force approach, PnP RANSAC, and filter matching distance of 0.3 on SIFT features.

关键词： visualization Matched filters Navigation Motion estimation Robot vision systems Pipelines Force

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Boosting Few-Shot Visual Learning With Self-Supervision

Boosting Few-Shot Visual Learning With Self-Supervision

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International conference on Computer Vision (ICCV)

作者： Spyros Gidaris Andrei Bursuc Nikos Komodakis Patrick Pérez Pérez Matthieu Cord valeo.ai LIGM Ecole des Pont ParisTech Valeo.ai Sorbonne Université

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns in the low data regime. Self-supervised learning focuses instead on unlabeled data and looks into it for the supervisory signal to feed high capacity deep neural networks. In this work we exploit the complementarity of these two domains and propose an approach for improving few-shot learning through self-supervision. We use self-supervision as an auxiliary task in a few-shot learning pipeline, enabling feature extractors to learn richer and more transferable visual representations while still using few annotated samples. Through self-supervision, our approach can be naturally extended towards using diverse unlabeled data from other datasets in the few-shot setting. We report consistent improvements across an array of architectures, datasets and self-supervision techniques. We provide the implementation code at: https://***/valeoai/BF3S.

关键词： Task analysis visualization Training Data models Image representation Biological system modeling Feature extraction

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Forte: An Interactive Visual Analytic Tool for Trust-Augment...

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Innovative Smart Grid Technologies (ISGT)

作者： Kaustav Bhattacharjee Soumya Kundu Indrasis Chakraborty Aritra Dasgupta Department of Data Science New Jersey Institute of Technology USA Optimization and Control Group Pacific Northwest National Laboratory USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

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Smart Finance: An overview of Artificial Intelligence Integration in Fintech

Smart Finance: An overview of Artificial Intelligence Integr...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Warsha Siraskar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion.

关键词： Ethics visualization Technological innovation Fintech Education Companies Data science Regulation Risk management Artificial intelligence

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Enhancing Image Clustering with CLIP

Enhancing Image Clustering with CLIP

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Cyber-Physical Social Intelligence (ICCSI), International conference on

作者： Mengjuan Li Wenming Cao Lei Zhang Man Li Mingming Yang School of Mathematics and Statistics Chongqing Jiaotong University Chongqing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

Both similarity of instances and difference among clusters play an essential role in improving the performance of deep image clustering. Existing deep clustering methods often fail to capture both consistency and distinctness of semantics when the label information is not available. In this work, we propose a semantic prototype pseudo-label-based image clustering model, which uses a CLIP image encoder as the feature model and incorporates an ensemble-based clustering head. The feature model is responsible for evaluating the consistency between instances, while the clustering prediction head is used to discern semantic differences. The prototype pseudo-label algorithm is designed to train the clustering head to effectively improve the accuracy and reliability of self-supervision. The optimization of the clustering network is divided into two stages, both of which do not rely on real labels: 1) The feature model is trained using contrastive loss to evaluate the consistency of instances; 2) The clustering head is trained using the prototype pseudo-label algorithm to distinguish between semantic categories. Comprehensive experimental evaluations demonstrate the effectiveness of our method in improving clustering performance, especially achieving a 10% improvement on three recognized evaluation metrics compared to existing techniques on the STL-10 and ImageNet-10 datasets.

关键词： Representation learning visualization Head Semantics Prototypes Clustering algorithms Self-supervised learning Predictive models Reliability engineering Social intelligence

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Hierarchical min-max octree applying adaptive blocks

Hierarchical min-max octree applying adaptive blocks

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International conference on e-health Networking, Applications and Services (HealthCom)

作者： Sukhyun Lim Byeong-Seok Shin Department Computer Science & Information Engineering Inha University Incheon South Korea

ISBN: (纸本)0780389409

There are several optimization techniques available for improving rendering speed of direct volume rendering. An acceleration method using the hierarchical min-max octree requires little preprocessing and data storage while preserving image quality. However, this method introduces computational overhead because of unnecessary comparison and level shift between blocks. In this paper, we propose an efficient space-leaping method using an hierarchical min-max octree applying adaptive blocks. To determine the size of blocks, our method partitions an image plane into several uniform grids, and computes the minimum and the maximum depth values for each grid. We acquire different-sized blocks suitable for individual rays from on these values. Experimental results show that our method reduces rendering time when compared with the previous hierarchical min-max octree method.

关键词： Rendering (computer graphics) Acceleration Image quality Computer science Data visualization Casting Data structures Computer errors Memory Grid computing

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Visual Quality Inspection Planning: A Model-Based Framework for Generating Optimal and Feasible Inspection Poses

Visual Quality Inspection Planning: A Model-Based Framework ...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Vanessa Staderini Tobias Glück Philipp Schneider Andreas Kugi Center for Vision Automation and Control AIT Austrian Institute of Technology GmbH Vienna Austria Automation and Control Institute Technische Universität Wien Vienna Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Automatic visual quality inspection is pivotal in both computer vision and robotics. It plays a crucial role in manufacturing, where robotic systems are increasingly employed to enhance the speed and efficiency of visual quality assessments. Several inspection planning methodologies have been developed; however, they often address the inspection challenge from a singular perspective of robotics or computer vision. This work introduces a comprehensive approach that synergistically integrates principles from both domains. We present an innovative algorithm designed to generate optimal inspection poses by considering the interplay between the inspected object’s geometry and the kinematics of the robotic setup used for inspection. This is accomplished by taking advantage of the concept of visibility. The effectiveness of our algorithm is demonstrated through simulations and experiments, revealing complete coverage for diverse geometries and materials with a small number of inspection poses. Moreover, we benchmark our framework against box constraints and workspace sampling techniques to generate feasible inspection poses. The results indicate superior performance in achieving extensive coverage and reducing the number of required optimal inspection poses, enhancing the overall inspection process.

关键词： Geometry visualization Computer vision Technological innovation Robot vision systems Reinforcement learning Inspection Planning Bayes methods optimization

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Advanced IoT Routing Algorithms for Improved Food Delivery Services with Temperature Control

Advanced IoT Routing Algorithms for Improved Food Delivery S...

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Advances in Data Engineering and Intelligent Computing Systems (ADICS), International conference on

作者： Abhijit Mitra Ramakrishnan Raman Symbiosis Law School (SLS) Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University) Pune Maharashtra India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Food delivery services have grown rapidly, making ensuring food quality and safety harder. This article investigates how Internet of Things (IoT) technologies might improve food delivery efficiency and dependability by improving routing methods and temperature management. It provides a unique delivery route optimization method that uses real-time GPS and traffic data from IoT devices. It reduces delivery times, fuel use, and environmental effects. Simulation and real-world testing show that the suggested routing algorithm outperforms the proposed techniques. Furthermore, it addresses the crucial problem of food delivery temperature management. IoT-enabled temperature sensors and control systems monitor and manage perishable food temperatures during delivery. It reduces food waste and improves food safety by keeping food products within temperature ranges. It performed comprehensive environmental tests to evaluate our temperature control system and examined the findings for temperature stability and food quality maintenance. IoT technology can improve food delivery efficiency, dependability and safety. For food delivery services aiming to improve operations using IoT, this paper offers insights and suggestions.

关键词： Temperature sensors visualization Routing Real-time systems Temperature control Safety Maintenance

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