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This paper presents a part of our ground-breaking work on evaluation of buildings in terms of wireless friendliness at the building-design stage. The main goal is to devise building design practices that provide for a good performance of wireless networks deployed in buildings. In this paper, the interference gain (IG) and power gain (PG) are defined as two figures of merit (FoM) of the wireless performance of buildings. The FoMs bridge the gap between building design and wireless communications industries. An approach to derive exact closed-form equations for these FoMs is proposed for the first time. The derived analytic expressions facilitate straightforward and more computationally efficient numerical evaluation of the proposed FoMs as compared to Monte Carlo simulations for well-known indoor propagation models. It is shown that the derived closed-form expression can be readily employed to evaluate the impact of building properties, such as the sizes and the aspect ratios (ARs) of rooms, on the wireless performance. The proposed approach sheds light to architects on evaluation and design of wireless-friendly building layouts. [ABSTRACT FROM AUTHOR]

Complex building structures constrain indoor wireless communications. The significant dependence between the indoor wireless network performance and the building structures can be considered as an intrinsic property of a building, which means that the building has to be designed to achieve desirable indoor wireless network performance. The power gain (PG) and the interference gain (IG) have been defined as the figures of merit (FoMs) to evaluate the wireless performance of a building. Employing open space as the benchmark, the PG and the IG respectively quantify the effective change of the intended signal power and the undesired power, i.e., the interference and thermal noise power, received in a targeted user equipment (UE) location due to the presence of the building. In this paper, a tractable approach is proposed to obtain the PG and the IG with a partition-based path gain model. It enables the PG and the IG to capture the impact of both building materials and the building layout on indoor wireless network performance. Numerical results show that the analytical models in closed-form expressions facilitate the wireless performance assessment to a given building computational-efficiently. This work is a critical complement to the initial framework of the building wireless performance (BWP) evaluation. The proposed method can be employed in future wireless-friendly building design. [ABSTRACT FROM AUTHOR]

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer. [ABSTRACT FROM AUTHOR]

This paper presents a phase-shifted dual-half-bridge converter, which achieves zero-voltage switching (ZVS) for the leading-leg switches and zero-current switching (ZCS) for the lagging-leg switches. The proposed converter contains two paralleled half-bridge inverters in the primary side. The rectifier stage is composed of six diodes connected with the form of full-bridge rectification. The ZVS condition is created by the output filter inductor and leakage inductor, while the ZCS condition is created by the blocking capacitors. A wide range of soft-switching operation can be achieved with small leakage inductors. Compared with the traditional phase-shifted full-bridge converter, the proposed converter can obtain a three-level output rectified voltage, and the power is transferred from the primary side to the secondary side during the whole period, which can significantly reduce the current ripple of the output filter inductor. In this paper, the operation principle and relevant analysis are presented. A laboratory prototype is built to verify the performance of the proposed converter. [ABSTRACT FROM PUBLISHER]

Two-stage isolated battery-driven grid interface (IBDGI) with front-end three-phase dual active bridge (DAB) dc–dc converter is promising to be applied in the residential battery energy storage system for supporting photovoltaic (PV) integration, load shifting, and backup power purposes. However, the second harmonic current (SHC) caused by the pulsating power of the downstream single-phase inverter will increase the battery's degradation and the component stress of the front-end converter. In this paper, a reduction method is proposed for the IBDGI with three-phase DAB converter using a load current feedforward control. The proposed idea is to incorporate virtual impedance to the output impedance of the front-end converter. The easy-to-be-implemented method does not need to modify the original voltage closed-loop controller but suppress SHC while keeping good dynamic performance. As the first step, the small-signal model of the three-phase DAB converter is provided. The parameter sensitivity analysis is proposed to demonstrate that the SHC can be well-suppressed within $\pm \text{20}\%$ parameter error of the feedforward control. Finally, a 1 kW experiment setup is built to verify the performance of the proposed SHC reduction method. [ABSTRACT FROM AUTHOR]

In this paper, a highly efficient isolated resonant converter with a novel modulation method is proposed for delivering power from photovoltaic (PV) modules to the dc microgrid. The proposed modulation method allows the converter to boost low input voltages and regulate a wide input voltage range. The converter design is based on a series resonant converter (SRC) that operates at the resonant frequency to achieve highest efficiency under nominal input voltage condition. Under shadowed or low irradiance conditions of PV panels, the converter will operate with the proposed “double-pulse duty cycle” modulation method to step up the voltage for the dc microgrid connection. With the proposed modulation method, the output switches serve for both synchronous rectification and voltage boost function. This method enables a higher voltage boost ratio than the SRC without adding additional switches while operating at the resonant frequency. A 300-W hardware prototype with gallium-nitride devices is built to verify the performance of the proposed converter and modulation method. The converter achieved a peak efficiency of 98.9% and a California Energy Commission weighted efficiency of 98.7% under nominal input voltage condition. [ABSTRACT FROM AUTHOR]

Wi-Fi-based localization using received signal strength (RSS) with pedestrian dead reckoning (PDR) algorithm is widely used to track pedestrians in indoor environments. However, the unsatisfactory deployment of Wi-Fi access points (APs) in buildings and the unstable performance of PDR are still key problems that lead to low localization accuracy. In this paper, we make contributions on proposing a hybrid Wi-Fi and Bluetooth Low Energy (BLE) indoor localization system (ILS) based on an efficient BLE deployment strategy and hierarchical topological fingerprinting (HTF). For the BLE deployment strategy, we deploy BLE beacons in places that do not have clear Wi-Fi signals for localization. This efficiently increases the localization accuracy. For HTF, we hierarchically localize targets based on a topological fingerprint (TF) map. First of all, we quickly localize the room in which the target is located by Dendogram-based support vector machine (DSVM). Then, the specific position of the target is estimated by fusing Wi-Fi and BLE signals with the TF map. The new BLE-based fingerprinting algorithm is used to localize targets in environments sparsely populated by BLE beacons. We conduct physical experiments in a real building. The experimental results demonstrate that the beacons deployed based on our proposed deployment strategy results in greater localization accuracy. Furthermore, the HTF approach performs better than the other commonly used localization methods. [ABSTRACT FROM AUTHOR]

Interactive learning plays a key role in extending the occupant behavior implementation toward smart buildings. Efficient feedbacks can be obtained from the end user by involving occupants and increasing their awareness about energy systems. Working in highly energy-efficient buildings can be a great opportunity, but users need to feel empowered. This means making them aware of the building features and allowing them to manage some of the appliances. In this way, disorientation or annoyance is avoided, and people feel more in control. This paper proposes a solution to interact with occupants to estimate the number of occupants. A novel way of supervised learning is analyzed to estimate the occupancy in a room where actual occupancy is interactively requested to occupants when it is the most relevant to limit the number of interactions. Occupancy estimation algorithm relies on machine learning; it uses information gathered from occupants with the measurements collected from common sensors, for instance, motion detection, power consumption, and CO2 concentration. Two different classifiers are investigated for occupancy estimation with interactions: a decision tree C4.5 and a parameterized rule-based classifier. In this paper, the question of when interacting with occupants is investigated. This approach avoids the usage of a camera to determine the actual occupancy. A complete real-time interaction environment has been developed and is used to estimate the occupancy in an office case study. The graphical user interface has been designed to carry out a real-time experiment.

This article explores the relatively underexplored potential for physicalizations to materialize qualitative data related to human experiences and knowledge domains. Our reading of "data" in this context extends from imperceptible systems and infrastructures to mental models and the phenomenological dimensions of experiences themselves. Physical objects can be regarded as a form of knowledge with which to inquire about human life, bring about improved conditions, and imagine alternative realities. Objects are made of materials, which are manipulated materials into various configurations. The materials used in the process of externalization have a profound influence on the resulting forms, and through them on how knowledge is constructed and internalized. We pay detailed attention to the characteristics of materials and how they are combined, in the context of interdisciplinary exchange. We are motivated by the need for a shared understanding of what work materials can do in the making of physicalisations. We suggest this work is useful in the analysis of physicalizations, specifically where they seek to articulate the phenomena of lived experience. [ABSTRACT FROM AUTHOR]

Utilizing an L-C-L network including first primary, a clamping capacitor and second primary, a phase-shift full-bridge converter with input current steering is designed and presented. This proposed converter inherits the advantages of the conventional phase-shift full bridge converter, including dv/dt noise attenuation via its zero voltage switching operation. Moreover, the current steering technique is realized with the help of a clamping capacitor implemented as a built-in second voltage source to share part of input current resulting in di/dt noise reduction. As a result, both di/dt and dv/dt noises can be attenuated and then electromagnetic interference (EMI) performance are improved. A conventional phase-shift full-bridge converter and the proposed converters are built for performance verification with an input from 300 to 390 V, switching frequency 100 kHz, and an output of 24 V, 15 A. Experimental result shows that the input current ripple of the proposed converter can be reduced in half to as small as 2.72 A, as opposed to the 5.20 A by the conventional converter, while the maximum EMI noise is successfully improved to 19.85 dBμV at 300 kHz. [ABSTRACT FROM AUTHOR]

In this article, two double flux modulation permanent-magnet machines (DFMPMs) with PMs on both stator and rotor are introduced. The two DFMPMs share a similar operation principle. However, due to the different stator-PM arrangements, the two DFMPMs show different performances. In order to analyze their performance characteristics, their analytical models are built and verified by finite element analysis (FEA). Moreover, to quantitatively compare their electromagnetic performances more accurately, the two DFMPM topologies are optimized, respectively, and compared in terms of back-EMF, cogging torque,rated torque, torque ripple, power factor, and efficiency. Finally, the DFMPM topology with relatively better performances is built and tested. The experimental results match well with the FEA simulations. [ABSTRACT FROM AUTHOR]

Buildings serve as the main places of human activities, and it is essential to automatically extract each building instance for a wide range of applications. Recently, automatic building segmentation approaches have made great progress in both detection and segmentation accuracy due to the rapid development of deep learning. However, these approaches struggle to delineate regular and accurate building boundaries due to the limitations in inferring overall structure of the building instance; this might lead to inconsistency in building geometry and difficulty in being applied directly to practical engineering. To tackle this challenge, this article presents an adaptive polygon generation algorithm (APGA), a novel method that aims at directly generating a polygonal output, parameterized as a sequence of building vertices, to outline each building instance. To achieve this, APGA predicts the candidate locations of building vertices and determines the arrangement of these vertices with the help of the position and orientation of the building boundary. Moreover, to introduce local context features and achieve improved performance of the predicted building polygon, APGA integrates finer structures around the candidate vertices to refine their positions. Experiments on several challenging building extraction datasets demonstrated that APGA outperformed state-of-the-art methods in terms of building coverage and geometric similarity. [ABSTRACT FROM AUTHOR]

A compact interlinking converter modular (ICM) for hybrid ac/dc microgrids with a distributed storage (DS) bus for storage integration is proposed in this article. The advanced ICM for hybrid ac/dc/ds microgrids involves the nine-switch topology with compact configuration and decreased number of active operating switches, which structurally equivalents to afford a dc interconnection port for dc subgrid, an ac interconnection port for ac subgrid, a ds interconnection port for ds subgrid, and two dc–dc converters for the interconnection ports for parallel DSs. In addition to the popular advantage of component elements saving, the suggested ICM, aggregating various interlinking converter cells (ICCs) into an ICM, is also proficient of participating computations from all ICC sensors to enhance the overall modular performance without building extra communication connection links between ICCs. With this in mind, a decentralized power management control strategy is realized by employing a coordination power control methodology to systematically handle various distributed generations (DGs), DSs, and ICM in a decentralized manner with improved communication fault ride-through capability. The controller hardware-in-the-loop experiments on a real-time digital simulator platform validates the achievement of the advanced ICM application in hybrid ac/dc/ds microgrids and the effectiveness of the associated decentralized power management scheme. [ABSTRACT FROM AUTHOR]