Applying ergonomics theories to analyze the postures of vehicle occupants and leveraging the principles of affective engineering and Analytic Hierarchy process (AHP), we design the form of comfortable car seats to mee...
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Accurate acquisition of displacement signals is of great importance in the field of engineering research, but displacement transducers have harsh requirements for testing conditions, which cannot be implemented in man...
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Industrial robots have been getting a more important role in manufacturing processes during the last decades, due to the flexibility they can provide in terms of reachability, size of working envelope and workfloor fo...
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The digital transformation era, emphasized by Industry 4.0, have transformed and globalized industries. This transformation is important for increasing the efficiency, traceability, and market reach, particularly in h...
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Initially pioneered in aircraft design to optimize maneuverability and aerodynamic efficiency, dynamic morphing technology is now revolutionized in ground vehicles to address the stability and maneuverability challeng...
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To improve the level of over-under excavation detection in construction process of mining tunnels, scholars demostic and abroad have combined 3D laser scanning with other information technology to achieve certain rese...
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The proceedings contain 100 papers. The special focus in this conference is on Product and process Modelling. The topics include: Integrating Level(s) LCA in BIM: A tool for estimating LCA and LCC impacts in a case st...
ISBN:
(纸本)9781032406732
The proceedings contain 100 papers. The special focus in this conference is on Product and process Modelling. The topics include: Integrating Level(s) LCA in BIM: A tool for estimating LCA and LCC impacts in a case study;How can LCA inform early-stage design to meet Danish regulations? The sustainability opportunity metric;life cycle potentials and improvement opportunities as guidance for early-stage design decisions;Structure and LCA-driven building design support in early phases using knowledge-based methods and domain knowledge;challenges and experiences with the reuse of products in building design;evaluating existing digital platforms enabling the reuse of reclaimed building materials and components for circularity;semantic Material Bank: A web-based linked data approach for building decommissioning and material reuse;NLP-based semantic model healing for calculating LCA in early building design stages;Construction product identification and localization using RFID tags and GS1 data synchronization system;what comes first when implementing data templates? Refurbishment case study;chaos and black boxes – Barriers to traceability of construction materials;evaluating four types of data parsing methods for machine learning integration from building information models;Extending ICDD implementation to a dynamic multimodel framework;Management of BIM-based digital twins in multimodels;Enriching BIM-based construction schedules with semantics using BPMN and LBD;a simulative framework to evaluate constructability through parameter optimization at early design stage;automatic generation of work breakdown structures for evaluation of parallelizability of assembly sequences;Construction process time optimization of a reinforced concrete reaction slab – Implementing the VDC methodology.
The purpose of this research is to investigate the incorporation of machine literacy techniques for the prediction of chip failure rates in the design of veritably Large Scale integration (VLSI) systems. The complexit...
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System-in-Package (SiP) is a technology that integrates multiple functional chips into a single substrate through micro-assembly, offering advantages such as compact size, high packaging efficiency, compatibility, and...
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ISBN:
(纸本)9798350395600;9798350395594
System-in-Package (SiP) is a technology that integrates multiple functional chips into a single substrate through micro-assembly, offering advantages such as compact size, high packaging efficiency, compatibility, and low power consumption. As a crucial control component for satellite in orbit, the spaceborne processor requires high performance integration and compactness. Utilizing SiP to form a microsystem with the spaceborne processor can efficiently fulfill these requirments. However, as the integration level and signal frequency increase, SiP must address signal integrity and power supply stability concerns to ensure its reliability. Furthermore due to the long-term reliability demands of aerospace SiP in specific environments, optimizing the packaging process is necessary, alongside enhancements in heat dissipation and mechanical stability. This paper presents the design of a microsystem SiP tailored for spaceborne processors, accompanied by simulation analysis to evaluate its signal integrity, power integrity, heat dissipation, and stress-all of which conform to the reference standards.
The increased integration of renewable energy, which is known to fluctuate owing to weather conditions, has necessitated power supply-demand adjustments using virtual power plants (VPPs) that utilize vehicle batteries...
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ISBN:
(纸本)9798331544461;9784907764838
The increased integration of renewable energy, which is known to fluctuate owing to weather conditions, has necessitated power supply-demand adjustments using virtual power plants (VPPs) that utilize vehicle batteries. Predictions of the available adjustment capacity of a VPP can prevent excessive power generation. Therefore, the state of charge (SOC) of individual vehicles participating in the supply-demand adjustment market must be predicted up to one week in advance. Because vehicle owners' actions determine SOC behavior, considering their periodicity and uncertainty is essential. This study proposes a novel probabilistic model called the matrix-variate Gaussian process (GP) with structured kernels, while focusing on the periodicity. To achieve high accuracy through recursive multi-step-ahead prediction using GP, large computational resources are required. However, the proposed method can handle a matrix with rows for the time of day and columns for the day of the week, which facilitates the production of a one-week-ahead predictive distribution through a single calculation. Furthermore, the proposed method directly captures the periodicity using separate kernels to express the characteristics of rows and columns, making the parameters easier to interpret. Finally, we evaluated the performance of the proposed method using real data from 100 vehicles over up to nine months.
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