THE THIRD PLACE: A MIXED-USE BUILDING FOR OFFICE WORKERS IN THE CENTRAL BUSINESS DISTRICT

Work-related stress is an issue of growing concern around the world.[1] The relationship between work stress and individuals’ psychological and physical health is well acknowledged.[2] In the survey of “stress in the workplace” conducted by American Psychological Association in 2012, two in five (41%) of employed adults report that they typically feel stressed during the workday, up from 36% in 2011, while less than six in ten (58%) report that they have the resources to manage work stress.[3] Historically, Central Business Districts are a focal point of cities, and are occupied by a large group of office buildings and a number of retail spaces. According to Elsbach and Bechky (2007), office workers regularly leave their offices in search of more relaxed, creative environments.[4] However, there is limited third space to serve office workers\u27 daily life in the central business district. Most of the design research about wellness of office workers have done focus on the spaces in which people work during office hours. Consideration for office workers in the CBD after office hours is relatively rare. What else is needed to support the life of the office worker, and what kind of spaces they are looking for after hours. Several primary research methods were adopted. First, a survey of research on how the design of traditionally planned CBDs fails to support wellness of office workers was made. Then, the thesis examined what is needed to support the wellness of office workers. In order to make it be specific to the office workers in the CBD of Richmond, qualitative methodology, including interviews and video records of the daily CBD living habits were made. In addition, case studies of recently done CBDs that tackle this question. The Shibaura House, designed by Kazuyo Sejima, located in the business district of Tokyo in Japan, will serve as a primary case study. There are three aims in this research. First is the design of a series of mixeduse spaces in an existing building in the Central Business District of Richmond to support the life of office workers after office hours. It also aims to improve wellness of the office workers in the CBD of Richmond, and try to define the CBD in a new way. The preliminary results for this research indicates the importance of the concern for office workers after hours. It is necessary to focus on the practical effect of the mixed-use building on reducing work stress, improving office workers’ health and enhancing wellness of office workers

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds

Automatic multi-modal tuning of idiophone bars

Idiophones generate sound through the vibration of their beam-like “keys”. The musical sound generated depends on the natural bending vibrations of the free-free beams. The tonal quality of the idiophone bar is achieved by tuning the second and third natural bending frequencies in relation to the fundamental natural frequency. Tuning these harmonic overtones becomes one of the primary tasks for making idiophone bars. It is achieved by removing material from the underside of the beams. This thesis focuses on the accurate prediction of the geometry of the beam underside (undercut) shape of marimba bars1 and the fine tuning process for correcting the unavoidable uncertainties of wood during automated tuning.The correct underside shape of the marimba bar was predicted using Timoshenko beam receptances. The underside shape predictive model predicts the resulting natural bending frequencies based on the undercut geometry of the bars. A search algorithm was implemented to find the correct geometry of the undercut for the multi-mode frequency requirements. A CNC machine tool was adapted to mill the specified underside shape from a wood blank, and this machine tool was combined with the predictive model and automatically controlled by the hardware controlling program. A fine tuning program was developed to incrementally approach the target natural frequencies from above, thereby correcting for the unknown non-homogeneity and anisotropy of wood.Manufacture of wooden bars showed that the underside shape predictive model was very accurate when the elastic properties of the test material are accurate. For non-homogeneous and anisotropic material the improvement of the actual results made by the fine tuning program were observed. A physical machining centre, which combines the underside shape predictive model, the fine tuning program, the hardware controlling program, the frequency measuring program and a self-built CNC machine, have been developed to automate the tuning process

Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis

Recent years have witnessed the great success of deep learning on various point cloud analysis tasks, e.g., classification and semantic segmentation. Since point cloud data is sparse and irregularly distributed, one key issue for point cloud data processing is extracting useful information from local regions. To achieve this, previous works mainly extract the points' features from local regions by learning the relation between each pair of adjacent points. However, these works ignore the relation between edges in local regions, which encodes the local shape information. Associating the neighbouring edges could potentially make the point-to-point relation more aware of the local structure and more robust. To explore the role of the relation between edges, this paper proposes a novel Adaptive Edge-to-Edge Interaction Learning module, which aims to enhance the point-to-point relation through modelling the edge-to-edge interaction in the local region adaptively. We further extend the module to a symmetric version to capture the local structure more thoroughly. Taking advantage of the proposed modules, we develop two networks for segmentation and shape classification tasks, respectively. Various experiments on several public point cloud datasets demonstrate the effectiveness of our method for point cloud analysis.Comment: Technical Repor

An empirical study of touch-based authentication methods on smartwatches

The emergence of smartwatches poses new challenges to information security. Although there are mature touch-based authentication methods for smartphones, the effectiveness of using these methods on smartwatches is still unclear. We conducted a user study (n=16) to evaluate how authentication methods (PIN and Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect authentication accuracy, speed, and security. Circular UIs are tailored to smartwatches with fewer UI elements. Results show that 1) PIN is more accurate and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are more secure but less accurate than Circular UIs; 4) display size does not affect accuracy or speed, but security; 5) Square PIN is the most secure method of all. The study also reveals a security concern that participants' favorite method is not the best in any of the measures. We finally discuss implications for future touch-based smartwatch authentication design.Comment: ISWC '17, Proceedings of the 2017 ACM International Symposium on Wearable Computers, 122-125, ACM New York, NY, US

In-wheel motor vibration control for distributed-driven electric vehicles:A review

Efficient, safe, and comfortable electric vehicles (EVs) are essential for the creation of a sustainable transport system. Distributed-driven EVs, which often use in-wheel motors (IWMs), have many benefits with respect to size (compactness), controllability, and efficiency. However, the vibration of IWMs is a particularly important factor for both passengers and drivers, and it is therefore crucial for a successful commercialization of distributed-driven EVs. This paper provides a comprehensive literature review and state-of-the-art vibration-source-analysis and -mitigation methods in IWMs. First, selection criteria are given for IWMs, and a multidimensional comparison for several motor types is provided. The IWM vibration sources are then divided into internally-, and externally-induced vibration sources and discussed in detail. Next, vibration reduction methods, which include motor-structure optimization, motor controller, and additional control-components, are reviewed. Emerging research trends and an outlook for future improvement aims are summarized at the end of the paper. This paper can provide useful information for researchers, who are interested in the application and vibration mitigation of IWMs or similar topics