Evolutionary Multi-objective Optimization in Building Retrofit Planning Problem

AbstractEnergy efficiency has been a primary subject of concern in the building sector, which consumes the largest portion of the world's total energy. Especially for existing buildings, retrofitting has been regarded as the most feasible and cost-effective method to improve energy efficiency. When planning retrofit in public buildings, the most obvious objectives are to: (1) minimize energy consumption; (2) minimize CO2 emissions; (3) minimize retrofit costs; and (4) maximize thermal comfort; and one must consider these concerns together. The aim of this study is to apply evolutionary multi-objective optimization algorithm (NSGA-III) that can handle four objectives at a time to the application of building retrofit planning. A brief description of the algorithm is given, and the algorithm is examined using a building retrofit project, as a case study. The performance of the algorithm is evaluated using three measures: average distance to true Pareto-optimal front, hypervolume, and spacing. The results show that this study could be used to find a comprehensive set of trade-off scenarios for all possible retrofits, thereby providing references for building retrofit planners. These decision makers can then select the optimal retrofit strategy to satisfy stakeholders’ preferences

A study on the characteristics of bridge bearings behavior by finite element analysis and model test

The increased vibration level of the railway bridge could make significant noise and, also, cause structural damages such as fatigue cracks. Related to these subjects, a spherical elastomeric bridge bearing, which is layered by hemispherical rubber and steel plates, was investigated in terms of its vibration performance. Several different shape factors could be considered by changing the curvature of hemispherical surface and size in rubber and steel plate thicknesses in the manufacturing stage. The performance of the spherical elastomeric bearing for the reduction in vibration was compared with that of the conventional bearing by performing vibration experiments on a scale-downed model. The rubber material characteristics and spherical shape are found to be important parameters in reducing the bridge vibration

Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies

Spatial data acquisition, integration, and modeling for real-time project life-cycle applications

Current methods for site modeling employs expensive laser range scanners that produce dense point clouds which require hours or days of post-processing to arrive at a finished model. While these methods produce very detailed models of the scanned scene, useful for obtaining as-built drawings of existing structures, the associated computational time burden precludes the methods from being used onsite for real-time decision-making. Moreover, in many project life-cycle applications, detailed models of objects are not needed. Results of earlier research conducted by the authors demonstrated novel, highly economical methods that reduce data acquisition time and the need for computationally intensive processing. These methods enable complete local area modeling in the order of a minute, and with sufficient accuracy for applications such as advanced equipment control, simple as-built site modeling, and real-time safety monitoring for construction equipment. This paper describes a research project that is investigating novel ways of acquiring, integrating, modeling, and analyzing project site spatial data that do not rely on dense, expensive laser scanning technology and that enable scalability and robustness for real-time, field deployment. Algorithms and methods for modeling objects of simple geometric shape (geometric primitives from a limited number of range points, as well as methods provide a foundation for further development required to address more complex site situations, especially if dynamic site information (motion of personnel and equipment). Field experiments are being conducted to establish performance parameters and validation for the proposed methods and models. Initial experimental work has demonstrated the feasibility of this approach

Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification

Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes. To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; however, it is still challenging due to the scarcity of medical data. In this study, we demonstrate that the pretrained model on large-scale visual and audio datasets can be generalized to the respiratory sound classification task. In addition, we introduce a straightforward Patch-Mix augmentation, which randomly mixes patches between different samples, with Audio Spectrogram Transformer (AST). We further propose a novel and effective Patch-Mix Contrastive Learning to distinguish the mixed representations in the latent space. Our method achieves state-of-the-art performance on the ICBHI dataset, outperforming the prior leading score by an improvement of 4.08%.Comment: INTERSPEECH 2023, Code URL: https://github.com/raymin0223/patch-mix_contrastive_learnin

Specific Inhibition of Soluble γc Receptor Attenuates Collagen-Induced Arthritis by Modulating the Inflammatory T Cell Responses

IL-17 produced by Th17 cells has been implicated in the pathogenesis of rheumatoid arthritis (RA). It is important to prevent the differentiation of Th17 cells in RA. Homodimeric soluble γc (sγc) impairs IL-2 signaling and enhances Th17 differentiation. Thus, we aimed to block the functions of sγc by inhibiting the formation of homodimeric sγc. The homodimeric form of sγc was strikingly disturbed by sγc-binding DNA aptamer. Moreover, the aptamer effectively inhibited Th17 cell differentiation and restored IL-2 and IL-15 signaling impaired by sγc with evidences of increased survival of T cells. sγc was highly expressed in SF of RA patients and increased in established CIA mice. The therapeutic effect of PEG-aptamer was tested in CIA model and its treatment alleviated arthritis pathogenesis with impaired differentiation of pathogenic Th17, NKT1, and NKT17 cells in inflamed joint. Homodimeric sγc has pathogenic roles to exacerbate RA progression with differentiation of local Th17, NKT1, and NKT17 cells. Therefore, sγc is suggested as target of a therapeutic strategy for RA