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Most engineers predict future building energy consumption via simulation programs in the pre-design phase. In this process, many simulation steps have to be repeated to predict building energy consumption. The authors in this article proposed another way to select optimal building materials for saving commercial building energy in the U.S. using soft computing methods. To achieve the research goal, reliable public data that is provided by the U.S. Energy Information Administration was used. The data contain numerous energyrelated characteristics of buildings including gas, electricity, types of materials, and climate conditions of 6,700 commercial buildings located in the U.S. This study utilized two methods to find out optimal building materials for saving energy. First, the Principle Component Analysis was used to determine which building characteristics among over 400 characteristics have the greatest impact on gas and electricity consumption. Second, Association Rule Mining was used to extract combinations of optimal building materials. Since a building consists of a combination of various materials, energy simulation should predict for multiple factors rather than a single factor. The use of these methods would greatly reduce resources, such as limited budget and time, during the simulation process.

We examined the risk of osteoarthritis (OA) according to vitamin D status and bone mineral density (BMD) using a cross-sectional nationally representative database.National Health and Nutrition Examination Survey (NHANES) data from 2007 to 2010 were used to assess the relationship between OA and vitamin D status in adults aged ≥40 years (Participants with serum 25OHD20 ng/mL had a 37% lower risk of OA (95% confidence interval (CI) [0.39-0.99],The risk of OA is lower in older men with 25OHD less than 20 ng/mL but not in older women. Bone mineral density is not associated with OA risk in older adults in the United States.

Purpose: Building design optimization could be performed in various ways. In most cases, potential design alternatives are evaluated against multi-criteria performance indicators through iterative create-test processes. Especially for the early design stage, optimum passive design of a building tends to be more critical and needs to be investigated rigorously. This study focuses on building passive design optimization in different global regions considering their climatic characteristics. Site location, building orientation, wall construction, and glazing assemblies are considered to be primary passive design variables. Objective functions for optimization include occupancy comfort, capital cost, and life-cycle cost. Method: By utilizing Design Builder’s genetic-algorithm-based optimization tool, a single-room type highly simplified spatial unit virtually being located in 5 different global regions (Jakarta, Cairo, Buenos Aires, Seoul, and Resolute) is evaluated and optimized against two essential design criteria. Result: It is turned out that passive building design in different climatic contexts may lead to a different set of strategies to minimize occupant’s discomfort and life-cycle cost. The research outcome shows that the lowest solar transmission glazing assembly, which is critical for optimum passive design strategy in cooling-dominated regions may not function properly in the locations where tropical hot-humid weather prevail.

Semi-transparent photovoltaic (STPV) windows, one of the building façade elements, can generate electricity and provide a certain amount of daylight for occupants. Nevertheless, expensive cost and unsatisfying indoor daylight performance in the room are common problems with STPV windows. This study investigates the thermal, daylight, energy, and life-cycle cost performance of STPV windows by considering varied window-to-wall ratios, building orientations, and STPV module types. The electricity balance index (elBI) indicator is proposed as one of the performance evaluation criteria. Two types of building models are established for this study: a rig-test building as the baseline building model and a KAIST campus research facility as the test building model along with the actual measurements and simulations using DesignBuilder. Results show that the STPV window in the Mediterranean climate demonstrates higher efficiency based on the elBI indicator. Decision-making analysis using the analytic hierarchy process and PROMETHEE II found weighting rates of 0.309, 0.076, and 0.465 for elBI, comfort, and cost criteria, respectively. Furthermore, lighting energy consumption becomes a critical variable for STPV module type selection, while a simple ON/OFF lighting control system can improve the elBI value by 0.02 ~ 0.04. Our research findings could potentially improve the decision-making process for building and urban energy systems selection in different climate types.

We propose an Internet of Things (IoT)-based occupancy detection system using change patterns of dust concentrations such as particulate matter. Previous research studies have used other features such as visual, chemical, or acoustic data. In this paper, the point extraction algorithm is proposed to construct triangular shapes, and their properties are used to detect occupancy in an indoor environment. For the verification of the proposed method, an IoT-based system is implemented for the occupancy detection in real residential environments. Finally, we analyze the experimental results, and compare them with those of other conventional approaches from a qualitative point of view.

Thermal comfort is a critical issue in achieving an acceptable indoor environment and managing building energy use. However, it is difficult to precisely recognize thermal comfort because its determination varies depending on the characteristics of humans and indoor spaces. Moreover, accumulating datasets of indoor environmental and individual features is challenging in terms of both collection time and cost, and is sometimes unrealistic. This study established a prediction model for individual thermal comfort to mitigate this challenge. This model is based on ensemble transfer learning (TL) to transfer knowledge from datasets of someone in different indoor spaces and thermal environments, even if the physiological and environmental data of the target subject are insufficient. First, the physiological data of each subject and the indoor environmental data were collected from wearable wristbands and sensors. Then, a pre-trained model was developed with the datasets by combining deep learning and machine learning algorithms. Based on the pre-trained model, the ensemble TL method was applied to overcome the weak generalization performance that occurred when the dataset of each target subject was insufficient. The results revealed that the ensemble TL more accurately predicted the thermal comfort of two target subjects using the pre-trained model from a source. The accuracy and F1-score were both 95% for the first subject. For the second subject, they were calculated as 85% and 83%, respectively. It was also found that the ensemble TL was suitable for application when using fewer and imbalanced datasets in the target domains.

The demand for efficient natural ventilation (NV) systems has increased for the development of sustainable buildings. However, the uncertainty of NV remains a challenging issue for appropriate utilization strategies of NV. For the successful implementation of NV systems in buildings, it is essential to clarify when and how to use NV systems in advance. In order to achieve the objectives, this study investigated the predictive models of NV rate (NVR) through eight machine learning (ML) algorithms, which are suitable for the interpretation of non-linear relationships between the measured indoor and outdoor environmental variables. Among all of the algorithms, deep neural network (DNN) ensured the best prediction performance for the NVR and it was shown that 40%, 46%, and 38% better predictive performance in terms of mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) than multivariate linear regression (MLR), which had the highest error rate, respectively. Based on the Shapley additive explanation (SHAP), the most influential features that affected to results of predictive models were examined and most of the ML approaches, except for MLR, had similar features (the pressure difference, outdoor temperature, wind speed, indoor relative humidity, solar radiation, the difference of indoor/outdoor relative humidity, and wind direction). The results of this study can improve the prediction performance for NVR, and this would contribute to the development of an intelligent NV system. Future work needs to develop the optimal operating strategies for hybrid ventilation systems integrating NV and mechanical systems.

The sequential Carnot cycle, where a number of single Carnot cycles are connected in parallel, has been researched for renewable thermal energy utilization because the characteristics of its low grade heat source, are different from those of a conventional one. In this paper, the thermodynamic analysis is conducted on the sequential Carnot cycle which uses not only finite heat sources but also finite heat sinks. Equations for efficiency and power from the whole sequential system are derived from the general theory of thermodynamics and heat transfer. Based on the equation from theory, the performance of the sequential system is calculated in various system conditions, including the ideal situation where the system has an infinite number of Carnot cycles and infinite heat exchanger inventory. In addition, the sequential concept is applied to organic Rankine cycles, which is one of the most used thermodynamic systems that generate work from low grade heat sources. From a simple simulation with properties of real working fluids, the performance of a sequential ORC (organic Rankine cycle) is investigated and its result is compared with that of theoretical sequential Carnot cycles.

As a recent dominant web trend, mobile first strategy has been emphasized, and interests with respect to responsive web that provides effective methodology to various mobile devices and their display resolution are also increasing. However, it is an early adoption stage in web mapping for geo-spatial applications. This study is to present some practical examples of layouts using responsive web technologies linked to geo-spatial open platform. Suggestive consideration points for further application of responsive web to geo-spatial services are presented. On test implementing, HTML5, JavaScript, and CSS3 were used, and web mapping processing using OpenLayers was performed. It is expected that responsive web applications for contents derived from geo-spatial information or location data increase.

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

In this paper, we propose a novel method for sentiment trend analysis using Ant Colony Optimization (ACO) algorithm and SentiWordNet. We first collect social data in the form of Resource Description Framework (RDF) triples, and then use ACO algorithm to digitize the amassed RDF triples. Using ACO algorithm, we then compute pheromone values to extract the trends of the user's sentiments with the modified equations. Next, we compute the user's sentiment scores for the computed pheromone values with respect to the sentiment words with SentiWordNet. Finally, we analyze the sentiment trend of the online user by time. For verification of the proposed method, we conduct experiments, and compare the analyzed sentiment trends with their real daily lives. The results show that the proposed method performs satisfactory sentiment trend analysis on real data.

Mobile devices including popular smartphone contributes to efficiency improvement of on-site data processing. Mobile environment for real-time data processing needs some additional aspects besides desktop ones, as the style of mobile app, mobile web and mobile web app. Generally, mobile app provides internal storage service so that offline data processing is possible. But mobile app needs separate development according to different types of devices or operating systems, even to different sizes of display panel of mobile devices. Geo-based data are composed of vector and raster formats from complex structure, compared to other data sets including image data and multimedia data. Hence, even though mobile applications development for geo-based data is more complicated than other ones, users' demands with respect to geo-based data processing functionalities on mobile environment are increasing in these days. Mobile web supported by technical basis of HyperText Markup Language5 (HTML5) is regarded as useful service type combining geo-based data processing modules and mobile environments, because it does not require user' downloading or installation of programs and just needs web browsers. Indexed DB Application Programming Interface (API) within this international web standard provides offline data storage functions on mobile environment. Using this API, data sets can be permanently stored into mobile devices, not cache memory. Among numerous geo-data functionalities, visualization topic is basically and commonly used in most mobile services. This study presents an implementation case of mobile web app with geo-based data visualization processing on online and offline mode. Types of geo-based data sets are base map of Open Street Map (OSM), OSM vector layers with Extensible Markup Language (XML) contents and high resolution satellite images of optical sensor. It is thought that the result of this implementation can play a role to create intelligent mobile application fields using both geo-based data sets and earth observation satellite image sets.

Ontology has been regarded as the core technology of the semantic web. However, non-experts still have difficulties in participating in ontology generation. So, the growing need for public participation in ontology generation has arisen. We propose a method in which the public may participate in ontology generation by adopting the ACO (Ant Colony Optimization) algorithm. We demonstrate that the ontology generated by the proposed method is satisfactory to justify our method: precision and recall of the ontology are about 94.44 and 99.6 % respectively. The suggested method enables the construction of the semantic web environment with non-experts in the field of ontology engineering.

In this paper, we propose an effective extraction method for acquiring the interests of users from Social Network Services (SNSs). In the proposed approach, a domain ontology generated by a decision tree is first used to classify domain webpages and each user. A Social Network Analysis (SNA) method is then used to analyze the tags from the Friend-Of-A-Friend (FOAF) profiles of each user; after which, we obtained the interests of the users. The results of an experiment conducted to obtain the interests of 2012 USA presidential candidates indicate that the precision and accuracy of our approach are 91.5 and 93.1 % in classifying the users, respectively.

The theoretical limit on Coefficient of performance (COP), which is maximum and ideal one, is investigated with a sequential Carnot heat pump where multiple Carnot heat pumps are interconnected in parallel. Using fundamental relations from thermodynamics and heat transfer, the performance of a sequential Carnot heat pump is obtained analytically. The effect of major parameters of the system, such as the number of individual heat pumps in a sequential system, the number of transfer unit of heat exchangers, and temperatures of heat sources, on the performance is researched. Also, expressions for ideal COP derived by limit calculation when the system has an infinite number of Carnot heat pumps or infinite number of transfer unit are suggested. For example, the most ideal COP is the ratio of the final heat source temperature to the difference between the initial and final heat source temperature. To support the result of theoretical analysis, a simple simulation of sequential Carnot heat pumps is carried out with thermodynamic properties of real refrigerants. The result of a simulation quite accords with that of theoretical analysis.