Optimal Packing of Irregular 3D Objects For Transportation and Disposal

This research developed algorithms, platforms, and workflows that can optimize the packing of 3D irregular objects while guaranteeing an acceptable processing time for real-life problems, including but not limited to nuclear waste packing optimization. Many nuclear power plants (NPPs) are approaching their end of intended design life, and approximately half of existing NPPs will be shut down in the next two decades. Since decommissioning and demolition of these NPPs will lead to a significant increase in waste inventory, there is an escalating demand for technologies and processes that can efficiently manage the decommissioning and demolition (D&D) activities, especially optimal packing of NPP waste. To minimize the packing volume of NPP waste, the objective is to arrange irregular-shaped waste objects into one or a set of containers such that container volume utilization is maximized, or container size is minimized. Constraints also include weight and radiation limits per container imposed by transportation requirements and the waste acceptance requirements of storage facilities and repositories. This problem falls under the theoretical realm of cutting and packing problems, precisely, the 3D irregular packing problem. Despite its broad applications and substantial potential, research on 3D irregular cutting and packing problems is still nascent, and largely absent in construction and civil engineering. Finding good solutions for real-life problems, such as the one mentioned above, through current approaches is computationally expensive and time-consuming. New algorithms and technologies, and processes are required. This research adopted 3D scanning as a means of geometry acquisition of as-is 3D irregular objects (e.g., nuclear waste generated from decommissioning and demolition of nuclear power plants), and a metaheuristics-based packing algorithm is implemented to find good packing configurations. Given the inefficiency of fully autonomous packing algorithms, a virtual reality (VR) interactive platform allowing human intervention in the packing process was developed to decrease the time and computation power required, while potentially achieving better outcomes. The VR platform was created using the Unity® game engine and its physics engine to mimic real-world physics (e.g., gravity and collision). Validation in terms of feasibility, efficiency, and rationality of the presented algorithms and the VR platform is achieved through functional demonstration with case studies. Different optimal packing workflows were simulated and evaluated in the VR platform. Together, these algorithms, the VR platform, and workflows form a rational and systematic framework to tackle the optimal packing of 3D irregular objects in civil engineering and construction. The overall framework presented in this research has been demonstrated to effectively provide packing configurations with higher packing efficiency in an adequate amount of time compared to conventional methods. The findings from this research can be applied to numerous construction and manufacturing activities, such as optimal packing of prefabricated construction assemblies, facility waste management, and 3D printing

Fine-Grained Access Control Systems Suitable for Resource-Constrained Users in Cloud Computing

For the sake of practicability of cloud computing, fine-grained data access is frequently required in the sense that users with different attributes should be granted different levels of access privileges. However, most of existing access control solutions are not suitable for resource-constrained users because of large computation costs, which linearly increase with the complexity of access policies. In this paper, we present an access control system based on ciphertext-policy attribute-based encryption. The proposed access control system enjoys constant computation cost and is proven secure in the random oracle model under the decision Bilinear Diffie-Hellman Exponent assumption. Our access control system supports AND-gate access policies with multiple values and wildcards, and it can efficiently support direct user revocation. Performance comparisons indicate that the proposed solution is suitable for resource-constrained environment

Headspace solid-phase microextraction and gas chromatography–mass spectrometry of volatile components of Chrysanthemum morifolium Ramat

Purpose: To extract and analyze the volatile components of Chrysanthemum morifolium Ramat. 'huaiju' by headspace solid-phase microextraction (HS-SPME) and gas chromatography–mass spectrometry (GC–MS).Methods: Volatile components were extracted by HS-SPME and identified by GC–MS. The relative contents of the components were determined by area normalization.Results: The enhanced SPME conditions of C. morifolium involved sample extraction using a 65 μm polydimethylsiloxane/divinylbenzene extraction fiber after balancing for 40 min at 80 °C. A total of 48 components of the essential oil were identified. The major constituents are 2,6,6-trimethylbicyclo[3.1.1]hept-2-en-4-ol, acetate (15.90 %), 4,6,6-trimethyl-bicyclo[3.1.1]hept-3-en-2-one (14.86 %), 2,7,7-trimethyl-bicyclo[3.1.1]hept-2-en-6-one (13.08 %), and cyclohexene,3-(1,5-dimethyl-4-hexenyl)-6- methylene (5.97 %).Conclusion: HS-SPME and GC–MS are convenient, rapid, and reliable approaches for analyzing the volatile components of C. morifolium.Keywords: Chrysanthemum morifolium Ramat., Headspace Solid-phase Microextraction, Gas Chromatography–Mass Spectrometry, Volatile componen

Online Power Measurement and Prediction of PCs

Abstract. Since more power consumption results in more failures and degradations in system performance, reliability, and power bills, it has been a critical problem for not only large scale server system but also personal computers (PCs). Though much literature has focused on energy management and power budgeting for server systems, power consumption of PCs does not attain sufficient attentions fairly. In this paper an online power measurement and prediction framework is proposed and used to save more energy considering the PC as a whole controlled system. The framework includes parts such as power measurement unit, power prediction unit and a simple execution unit of power reduction decisions. A hardware-software joint prototype is implemented based on an intelligent digital multimeter. Experiments on a desktop PC and a laptop show that PC with the framework can save more power consumptions than that of the PCs without this framework

Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation

Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods are unsatisfactory on real datasets where the new target domain comprises of heterogeneous sub-domains (e.g., diverse weather characteristics). We point out that carefully reasoning about the multiple modalities in the target domain can improve the robustness of adaptation models. To this end, we propose a condition-guided adaptation framework that is empowered by a special attentive progressive adversarial training (APAT) mechanism and a novel self-training policy. The APAT strategy progressively performs condition-specific alignment and attentive global feature matching. The new self-training scheme exploits the adversarial ambivalences of easy and hard adaptation regions and the correlations among target sub-domains effectively. We evaluate our method (DCAA) on various adaptation scenarios where the target images vary in weather conditions. The comparisons against baselines and the state-of-the-art approaches demonstrate the superiority of DCAA over the competitors.Comment: Accepted to AAAI 202