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Optimizing Integrated Shading Device and Light Shelf for Daylight Performance and Visual Comfort in Architecture Studio

Optimizing Integrated Shading Device and Light Shelf for Day...

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International conference on Environment and Electrical Engineering (EEEIC)

作者： Pelin Avcı Berk Ekici Zehra Tuğçe Kazanasmaz Department of Architecture Izmir Institute of Technology Izmir Turkey

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

To provide a sustainable interior, it is essential to consider visual comfort and energy efficiency for the occupants' well-being. Daylight is crucial in providing visual comfort while proposing energy-efficient design alternatives. Using daylight as a primary source is one of the most crucial strategies. However, controlling daylight for unwanted situations such as discomfort glare is important. There have been several research studies on daylighting, visual comfort, and shading techniques. Shading devices are façade configurations to control daylight, while light shelves distribute daylight evenly through the space. There are two types of classifications for shading devices: adaptive ones and non-adaptive ones. Numerous research studies have been conducted on daylighting, energy consumption, occupancy performance, and shading systems. Shading technologies, whether adaptive or not, provide benefits and drawbacks. Even though optimizing them is one way to design non-adaptive shading devices, they require minimal maintenance. This study aims to integrate adaptive shading devices and light shelves for university campus buildings to provide lighting design strategies. The aim is to create a study environment that promotes well-being and academic achievement. To pursue this study, three optimization algorithms were run to find the nearly optimal solution. The goal was to both maximize Daylight Autonomy and uniformity values. Results showed that HypE and SPEA2 results discovered near-optimal DA above 75% and uniformity between 0.6 and 0.7.

关键词： Performance evaluation visualization Energy consumption Daylighting Architecture Buildings Europe Energy efficiency Maintenance optimization

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SALIENCY-COGNIZANT ROBUST VIEW SYNTHESIS IN FREE VIEWPOINT VIDEO STREAMING

SALIENCY-COGNIZANT ROBUST VIEW SYNTHESIS IN FREE VIEWPOINT V...

引用

IEEE International conference on Image Processing

作者： Bruno Macchiavello Camilo Dorea Edson M. Hung Gene Cheung Wai-tian Tan Universidade de Brasilia Brazil National Institute of Informatics Japan Hewlett-Packard Laboratories USA.

ISBN: (纸本)9781479923427

In free viewpoint video, texture and depth maps from two camera-captured viewpoints are transmitted, so that at receiver, a novel virtual view chosen by the client can be synthesized via depth-image-based rendering (DIBR). When irrecoverable packet losses occur during transmission--typically affecting less important spatial regions in the video given unequal error protection (UEP) is deployed--appropriate error concealment strategies must be used at decoder to minimize resulting visual degradation in the synthesized view. Towards this goal, we propose a new optimization framework based on visual saliency to combine two different concealment techniques. First, given a pixel in the virtual view is typically constructed as a convex combination of corresponding pixels in the left and right captured views, weighted pixel blending (WPB) readjusts the weights in the linear sum to reflect the expected error in code blocks that contain the corresponding pixels. Second, exemplar-based patch matching (EPM) finds the most similar patches in the known spatial region to complete missing pixels in the unknown region. To choose between candidates constructed using the two techniques when filling a given pixel patch in the synthesized view, we first compute a weighted sum of expected error and visual saliency for each candidate patch. The candidate with the smaller sum (one with small expected error and visual saliency, so that even if errors do occur, they do not stand out visually) is selected for pixel completion. Experimental results show that our scheme can outperform the use of co-located blocks from a previous frame by up to 0.7dB in PSNR and improve subjective visual quality.

关键词： free viewpoint video visual saliency error concealment pixel Videotapes visualization View Data Security Packet loss indium Peak signal-to-noise ratio

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Parallel Medical Imaging: An ACP-Based Approach for Intelligent Medical Image Recognition with Small Samples

Parallel Medical Imaging: An ACP-Based Approach for Intellig...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International conference on

作者： Tianyu Shen Chao Gou Jiangong Wang Jun Huang Yonglan He Huadan Xue Zhengyu Jin Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Guangzhou China Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China

The deep learning methods trained with large-scale manually annotated datasets have led to significant breakthroughs in medical image community. However, obtaining such datasets remains a challenging work in medical domain. In this paper, we propose an ACP-based approach named parallel medical imaging (PMI) for analyzing and addressing the small sample problem of medical image recognition. Firstly, we present the basic framework and key techniques of PMI, in which the Artificial imaging systems are constructed to effectively augment the “medical small sample”, the Computational experiments are carried out for designing and evaluating the visual models trained with small samples, and the Parallel execution is conducted to achieve closed-loop interaction and optimization in the domain-knowledge guidance. Furthermore, we illustrate a preliminary implementation and application of PMI approach through the case study of mammogram analysis.

关键词： Deep learning visualization Image recognition Annotations Digital twin conferences Computational modeling

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Prefiltering and reconstruction filters in the volume rendering

Prefiltering and reconstruction filters in the volume render...

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Mediterranean Electrotechnical conference (MELECON)

作者： A. Goluban I. Mihajlovic L. Budin Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

This paper deals with reconstruction filters and appropriate prefiltering techniques in the volume rendering. Sampling, resampling and reconstruction of continuous functions are used in many different fields. To satisfy conditions of Shannon's theorem, prefiltering of images or data volumes is usually required before sampling. The usual approach is to use a Gaussian prefilter, where the cutoff frequency is determined by the sampling rate, and one of the different reconstruction schemes is then applied. The problem is to determine a prefilter so that the reconstruction will reproduce the original object as accurately as possible. If the combination of prefiltering and reconstruction in the volumetric space is not adequate, aliasing artifacts will appear. Aliasing artifacts project from the three-dimensional space onto the projection plane and superpose to the artifacts caused by sampling and reconstruction in two dimensions. We present a methodology for designing filters based on optimization of combined operation of prefiltering and reconstruction postfiltering. This methodology offers a framework for comparing different reconstruction schemes in equal circumstances. The correct choice of reconstruction will also influence the derivative reconstruction which is used for shading. We demonstrate our results on examples in two- and three-dimensional space.

关键词： Filters Image reconstruction Sampling methods Rendering (computer graphics) Image sampling Interpolation Spline Design methodology Data visualization Lighting

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Visual attention modeling for 3D video using neural networks

Visual attention modeling for 3D video using neural networks

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International conference on 3D Imaging (IC3D)

作者： Iana Iatsun Mohamed-Chaker Larabi Christine Fernandez-Maloigne Universite de Poitiers Poitiers Poitou-Charentes FR XLIM-SIC lab University of Poitiers France

Visual attention is one of the most important mechanisms in the human visual perception. Recently, its modeling becomes a principal requirement for the optimization of the image processing systems. Numerous algorithms have already been designed for 2D saliency prediction. However, only few works can be found for 3D content. In this study, we propose a saliency model for stereoscopic 3D video. This algorithm extracts information from three dimensions of content, i.e. spatial, temporal and depth. This model benefits from the properties of interest points to be close to human fixations in order to build spatial salient features. Besides, as the perception of depth relies strongly on monocular cues, our model extracts the depth salient features using the pictorial depth sources. Since weights for fusion strategy are often selected in ad-hoc manner, in this work, we suggest to use a machine learning approach. The used artificial Neural Network allows to define adaptive weights based on the eye-tracking data. The results of the proposed algorithm are tested versus ground-truth information using the state-of-the-art techniques.

关键词： Three-dimensional displays Feature extraction visualization Computational modeling Mathematical model Solid modeling Equations

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Robust Visual Object Tracking in Clustered Environment

Robust Visual Object Tracking in Clustered Environment

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International conference on Contemporary Computing and Informatics (IC3I)

作者： L.Krishna Kumari K. Ramalakshmi V. SrinivasaRaghavan Ramco Institute of Technology Rajapalayam India PSR Engineering College Sivakasi India Theni Kammavar Sangam College of Technology Koduvilarpatti Theni

To perform visual object tracking in clustered environment, kernel methods based Support Vector Machines (SVMs) can be used. A Scale adaptive Kernel support correlation filter Algorithm (SKSCF) is employed here for visual object tracking. In this method, the SVM models with circulant matrix formulation have been used. For visual tracking and its related applications it enhances the process of optimization. SVM models with circulant matrix formulation use the discrete Fourier transform operation to accomplish visual object tracking. The issue of visual objects is illustrated as a recursive command of Support Correlation Filters (SCFs). A Scale Adaptive Kernel Support Correlation Filter (SKSCF) has an O(n2 log n) computational complexity. The SVM-based techniques’ computational complexity is at O (n4). Along with the Scale Adaptive Kernel Support Correlation Filter Algorithm, factors like multi-channel features, scale adaptive approach, and kernel methods are applied. For a sizable standard dataset, the Scale Adaptive Kernel Support Correlation Filter Algorithm provides finer outcomes in terms of reliability and speed.

关键词： Support vector machines visualization Adaptation models Correlation Clustering algorithms Filtering algorithms Object tracking

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Object Classification with Adaptable Regions

Object Classification with Adaptable Regions

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IEEE conference on Computer Vision and Pattern Recognition

作者： Hakan Bilen Marco Pedersoli Vinay P. Namboodiri Tinne Tuytelaars Luc Van Gool ESAT-PSI-VISICS/iMinds KU Leuven Dept. of Comp. Sci. & Eng. IIT Kanpur

ISBN: (纸本)9781479951192

In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels. Unlike these works, in this paper, we propose to enhance the semantic representation of an image. We aim to learn the most important visual components of an image and how they interact in order to classify the objects correctly. To achieve our objective, we propose a new latent SVM model for category level object classification. Starting from image-level annotations, we jointly learn the object class and its context in terms of spatial location (where) and appearance (what). Furthermore, to regularize the complexity of the model we learn the spatial and co-occurrence relations between adjacent regions, such that unlikely configurations are penalized. Experimental results demonstrate that the proposed method can consistently enhance results on the challenging Pascal VOC dataset in terms of classification and weakly supervised detection. We also show how semantic representation can be exploited for finding similar content.

关键词： Training Support vector machines Vectors visualization optimization Context Encoding

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Optimal AI Models for Process Data Analysis in Industry 4.0 Regarding Digital Twin Implementation

Optimal AI Models for Process Data Analysis in Industry 4.0 ...

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International conference on Information Technology (INCIT)

作者： Tanat Kanangnanon Chalermpan Fongsamut Prajaks Jitngernmadan Faculty of Informatics Burapha University Chon Buri Thailand Faculty of Engineering Burapha University Chon Buri Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This study compares YOLOv8 and Mask R-CNN object detection algorithms for defect detection in digital twin applications for smart manufacturing. With the ongoing transformation of manufacturing processes in Industry 4.0 and 5.0, integrating digital twin technology and advanced visual inspection techniques has become crucial for improving quality control and process optimization. Using the expanded MVTec Metal Nut Dataset, we evaluated performance based on mean average precision (mAP), precision, recall, F1-score, and inference time. Results show YOLOv8 consistently outperformed Mask R-CNN in accuracy and computational efficiency, achieving higher scores across all metrics and quicker inference times. This suggests YOLOv8 is more suitable for real-time industrial applications, particularly in digital twin implementations. However, the study emphasizes the importance of considering specific manufacturing contexts when selecting an object detection model. The integration of AI-powered visual inspection with digital twin technology has significant potential to enhance product quality, reduce downtime, and optimize production processes, contributing valuable empirical evidence to guide the implementation of advanced defect detection systems in industry.

关键词： visualization Object detection Quality control Inspection Real-time systems Digital twins Fourth Industrial Revolution Quality assessment Defect detection Smart manufacturing

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Real Time Crop Harvest Time Prediction Model Using Raspberry Pi and Image Processing

Real Time Crop Harvest Time Prediction Model Using Raspberry...

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Advancement in Technology (ICONAT), International conference for

作者： Mrunalini S. Bhandarkar Basudha Dewan Payal Bansal Poornima University Jaipur India Poornima College of Engineering Jaipur India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Agriculture is a vital sector for ensuring global food security and promoting sustainable development in every country. Additionally, accurate prediction of crop yield and best harvest time is vital as it will help the farmers to maximize their profit with right utilization of the available resources. The proposed system enables prediction of crop harvest time using Raspberry Pi and image processing technique. The Raspberry Pi, which has a camera module installed, takes pictures of the crops over time. Image analysis techniques are applied to assess the visual indicators of crop maturity, such as color, size, and texture. This technology forecasts the best time to harvest by comparing these visual cues with past data. This approach eliminates the need for manual inspection and enables timely decision-making. The system incorporates a user-friendly interface accessible through a web or mobile application, presenting real-time and predicted harvest data. Notifications alert users to optimal harvest conditions. Continuous improvement is achieved through periodic updates and refinement of the image analysis algorithms, ensuring accurate predictions and adaptability to different crop types. This technique offers a practical and efficient solution for determining the ideal harvest time, facilitating resource optimization, and maximizing crop yields.

关键词： visualization Image analysis Image color analysis Decision making Crops Predictive models Real-time systems Sustainable development Crop yield Farming

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Human-centric Quality Management of Immersive Multimedia Applications

Human-centric Quality Management of Immersive Multimedia App...

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IEEE conference on Network Softwarization (NetSoft)

作者： Sam Van Damme Maria Torres Vega Filip De Turck IDLab Ghent University - imec

ISBN: (数字)9781728156842

ISBN: (纸本)9781728156859

Augmented Reality (AR) and Virtual Reality (VR) multimodal systems are the latest trend within the field of multimedia. As they emulate the senses by means of omnidirectional visuals, 360° sound, motion tracking and touch simulation, they are able to create a strong feeling of presence and interaction with the virtual environment. These experiences can be applied for virtual training (Industry 4.0), tele-surgery (healthcare) or remote learning (education). However, given the strong time and task sensitiveness of these applications, it is of great importance to sustain the end-user quality, i.e. the Quality-of-Experience (QoE), at all times. Lack of synchronization and quality degradation need to be reduced to a minimum to avoid feelings of cybersickness or loss of immersiveness and concentration. This means that there is a need to shift the quality management from system-centered performance metrics towards a more human, QoE-centered approach. However, this requires for novel techniques in the three areas of the QoE-management loop (monitoring, modelling and control). This position paper identifies open areas of research to fully enable human-centric driven management of immersive multimedia. To this extent, four main dimensions are put forward: (1) Task and well-being driven subjective assessment; (2) Real-time QoE modelling; (3) Accurate viewport prediction; (4) Machine Learning (ML)-based quality optimization and content recreation. This paper discusses the state-of-the-art, and provides with possible solutions to tackle the open challenges.

关键词： Quality of experience Measurement visualization Task analysis Quality of service Feeds Real-time systems

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