IMPLEMENTASI MULTIMEDIA BASED LEARNING VIA SELF VIDEO RECORDING (SVR) UNTUK MENINGKATKAN KEMAUAN SISWA SMKN 2 BANDAR LAMPUNG DALAM BERKOMUNIKASI

One of the keys to the success of learning English is the growth of students' willingness to actively speak in order to implement the theory they have learned. As is usually a skill, if you don't get used to it, it will definitely feel heavy and difficult. That's what happened to class XII DPIB 2 students, where on average they were reluctant to speak, either asking or answering the teacher's questions. They are passive, and mostly silent. There are many things behind their fear such as fear of being wrong, fear of shame, and lack of confidence due to lack of vocabulary and speaking practice. This study aims to increase students' willingness to implement learning using Self Video Recording (SVR) to increase students' willingness to speak and what aspects are most influenced by SVR. This research was conducted at SMK Negeri 2 Bandar Lampung for two months, namely January and February 2019 for class XI students majoring in Information and Building Modeling Design (DPIB). The author uses a one-group pretest-posttest design, where students are presented with questions on a questionnaire about willingness to communicate. The questionnaire contains 25 questions that are able to reveal in what situations they want to communicate in English class. To achieve the goal, the writer did 1x pretest, 3x treatment, and 1x posttest. After comparing the results of the pretest and posttest, the test hypothesis which states that the implementation of learning using self video recording can show a significant difference before and after the implementation of learning as long as the T-value > T-table with a significant level below 0.05. ABSTRAKSalah satu kunci keberhasilan pembelajaran Bahasa Inggris adalah tumbuhnya kemauan siswa untuk aktif berbicara guna mengimplementasikan teori yang telah mereka dapatkan. Sebagaimana lazimnya sebuah keahlian, jika tanpa dibiasakan pasti akan terasa berat dan sulit. Begitulah yang terjadi pada siswa kelas XII DPIB 2, dimana rata-rata mereka enggan berbicara, baik bertanya ataupun menjawab pertanyaan guru. Mereka pasif, dan lebih banyak diam. Ada banyak hal yang melatarbelakangi ketakutan mereka seperti takut salah, takut malu, dan tidak punya kepercayaan diri karena minimnya kosa kata dan latihan bicara. Pada penelitian ini bertujuan untuk meningkatkan kemauan siswa dengan implementasi pembelajaran menggunakan Self Video Recording (SVR) dapat meningkatkan kemauan siswa dalam berbicara dan aspek apakah yang paling banyak dipengaruhi oleh SVR. Penelitian ini dilaksanakan di SMK Negeri 2 Bandar Lampung selama dua bulan, yaitu Januari dan Februari 2019 pada siswa kelas XI jurusan Desain Pemodelan Informasi dan Bangunan (DPIB). Penulis menggunakan desain one-group pretest-posttest, dimana para siswa disajikan pertanyaan-pertanyaan pada sebuah kusioner tentang kemauan berkomunikasi (willingness to communicate). Kuesionaer tersebut berisi 25 pertanyaan yang mampu mengungkap pada situasi seperti apa mereka mau berkomunikasi pada kelas Bahasa Inggris. Untuk mencapai tujuan, penulis melakukan 1x pretest, 3 x perlakuan, dan 1x posttest. Setelah membandingkan hasil pretest dan posttest, maka hipotesa tes yang menyatakan bahwa implementasi pembelajaran menggunakan self video recording dapat menunjukkan perbedaan yang signifikan sebelum dan sesudah pelaksanaan pembelajaran selama T-value > T-table dengan tingkat signifikan dibawah 0,05.

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation.

A Descriptive 3D City Information Model Built From Infrastructure BIM

This study developed a descriptive 3D city information model (CIM) using only infrastructural building modeling tools to create maps, and analyzed the model according to needs identified in interviews with public-sector actors and a bibliometric analysis. The interviews assessed the challenges of implementing CIM in the Brazilian city of Curitiba, while the literature study determined that current academic production reflects the current reality, calling attention to relevant issues. The experimental software solution successfully created 3D informational modeling of cities for passive use as well as maps to support decision making, although it did not offer advanced parametric tools for urban analysis. Still, this model provides a flexible approach to overcoming the challenges reported by interviewees, which included financial limitations and organizational culture.

A descriptive 3D city information model built from infrastructure BIM

This study developed a descriptive 3D city information model (CIM) using only infrastructural building modeling tools to create maps, and analyzed the model according to needs identified in interviews with public-sector actors and a bibliometric analysis. The interviews assessed the challenges of implementing CIM in the Brazilian city of Curitiba, while the literature study determined that current academic production reflects the current reality, calling attention to relevant issues. The experimental software solution successfully created 3D informational modeling of cities for passive use as well as maps to support decision making, although it did not offer advanced parametric tools for urban analysis. Still, this model provides a flexible approach to overcoming the challenges reported by interviewees, which included financial limitations and organizational culture.

Formulation and evaluation of a composite acoustic objective incorporating air-borne and structure-borne transmission loss for optimization of building components

Technological advancements in computational building modeling have enabled designers to conduct many simulations at both the building and component levels. With the evolution of parametric modeling at the early stage of building design, designers can evaluate multiple design options and identify the best performing solutions. However, to conduct design space exploration or optimization, an objective function is needed to evaluate a design's performance. While defined objectives exist for building design considerations such as sustainability, energy usage, and structural performance there is not a single, encompassing objective that can accurately assess acoustic performance for optimization. This paper proposes the development of a novel acoustic objective function that encompasses sound transmission when designing floors, walls, or other acoustic barriers. The composite function will incorporate both air-borne and structure-borne sound simultaneously to determine the appropriate percentages for the formulation of the composite function. The results of the composite acoustic function for multiple floor constructions will be compared for the determination of a final acoustic transmission composite function. This study will detail why the implementation of a composite acoustic function is valuable for design optimization for sound transmission, what the limitations of this method are, and future applications of a composite acoustic function.

SYNTHETIC DATA GENERATION PIPELINE FOR GEOMETRIC DEEP LEARNING IN ARCHITECTURE

With the growing interest in deep learning algorithms and computational design in the architectural field, the need for large, accessible and diverse architectural datasets increases. Due to the complexity of such 3D datasets, the most widespread techniques of 3D scanning and manual building modeling are very time-consuming, which does not allow to have a sufficiently large open-source dataset. We decided to tackle this problem by constructing a field-specific synthetic data generation pipeline that generates an arbitrary amount of 3D data along with the associated 2D and 3D annotations. The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision. Creating our building data generation pipeline we leveraged the experts’ architectural knowledge in order to construct a framework that would be modular, extendable and would provide a sufficient amount of class-balanced data samples. Moreover, we purposefully involve the researcher in the dataset customization allowing the introduction of additional building components, material textures, building classes, number and type of annotations as well as the number of views per 3D model sample. In this way, the framework would satisfy different research requirements and would be adaptable to a large variety of tasks. All code and data is made publicly available: https://cdinstitute.github.io/Building-Dataset-Generator/.

TOWARDS EFFICIENT INDOOR/OUTDOOR REGISTRATION USING PLANAR POLYGONS

The registration of indoor and outdoor scans with a precision reaching the level of geometric noise represents a major challenge for Indoor/Outdoor building modeling. The basic idea of the contribution presented in this paper consists in extracting planar polygons from indoor and outdoor LiDAR scans, and then matching them. In order to cope with the very small overlap between indoor and outdoor scans of the same building, we propose to start by extracting points lying in the buildings’ interior from the outdoor scans as points where the laser ray crosses detected façades. Since, within a building environment, most of the objects are bounded by a planar surface, we propose a new registration algorithm that matches planar polygons by clustering polygons according to their normal direction, then by their offset in the normal direction. We use this clustering to find possible polygon correspondences (hypotheses) and estimate the optimal transformation for each hypothesis. Finally, a quality criteria is computed for each hypothesis in order to select the best one. To demonstrate the accuracy of our algorithm, we tested it on real data with a static indoor acquisition and a dynamic (Mobile Laser Scanning) outdoor acquisition.

Decentralized Management of Commercial HVAC Systems

With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling.