SourceAmerica Design Challenge Accessible Kitting and Packaging Station

This document entails our research, design, proposed development, and testing process for solving the 2020 SourceAmerica collegiate design challenge. Our team, “Just Kitting”, is composed of four Mechanical Engineering students from California Polytechnic San Luis Obispo. The design challenge requires us to create a device that will help improve the quality of life and productivity of people with disabilities working in the kitting and packaging industry. This document includes our background research and information received from various interviews with our sponsor and others who have experience working with disabilities. Using this information, we refined our problem statement to focus on individuals with disabilities that affect their fine motor skills because many procedures in the kitting and packaging industry are heavily reliant on the dexterity of the user. We tailored our ideation process, decision matrices, concept prototypes, and design justification around this target demographic. This process resulted in the final design of our workstation which provides an innovate and efficient way to bag and package five types of items. In addition, this design requires simple push-pull motions to reduce the dexterity required to create a kit. We have outlined our manufacturing and design verification plans to proceed with this design, along with a breakdown of our projected costs to implement a functional prototype

High-Power-Density Single-Phase Three-Level Flying-Capacitor Buck PFC Rectifier

Active pulsating-power-buffering (PPB) is an effective technique to reduce the energy storage requirement of a single-phase power-factor-correction (PFC) rectifier. Existing single-phase solutions with active PPB, however, generally suffer from high voltage stresses, leading to increased power losses as well as the need for high-voltage-rating semiconductor switches. Previous works have been focusing on two-level switching converter configurations, and thus have failed to address the high-voltage-stress problem. In this paper, a single-phase three-level flying-capacitor PFC rectifier with PPB embedded switching is proposed. The flying capacitor not only clamps the voltage stresses of all power devices but also functions as a PPB capacitor. The operating principles, control methods, and design guidelines are detailed and the feasibility of the proposed converter is verified through a 48-W (48-V/1-A) hardware prototype. The proposed rectifier is shown to achieve nearly 50% reduction of the voltage stresses, 72% reduction of the buffering capacitor's volume and 23.8% reduction of the magnetic core size, as compared to a state-of-the-art two-level solution recently proposed. This new approach of formulating single-phase PFC rectifiers with active PPB could dramatically boost the system's efficiency and power density whilst reducing cost

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to a undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds-10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases

Internal Dynamics Stabilization of Single-Phase Power Converters with Lyapunov-Based Automatic-Power-Decoupling Control

Single-phase power converters with the active pulsating-power-buffering (PPB) function are essentially highly coupled and nonlinear systems. Advanced control techniques are needed for this emerging class of converters to achieve fast transient response and large-signal stability. Existing control solutions are based on either 1) linear control techniques that are operating-point specific or 2) nonlinear control techniques that are generally topology-dependent. The proposed work is an evolved generalized feedback-linearization (FBL) control approach that incorporates the direct Lyapunov control method. The proposed control provides good stabilization of the internal dynamics of the system (which is unviable with FBL control) while still retaining all the best features of FBL control. A kind of single-phase power conversion system with active PPB is described. It is shown that FBL control naturally destabilizes the system and that the proposed control can globally stabilize the system under various operating conditions while yielding fast dynamics.</p

A Research Review on the Key Technologies of Intelligent Design for Customized Products

The development of technologies such as big data and cyber-physical systems (CPSs) has increased the demand for product design. Product digital design involves completing the product design process using advanced digital technologies such as geometry modeling, kinematic and dynamic simulation, multi-disciplinary coupling, virtual assembly, virtual reality (VR), multi-objective optimization (MOO), and human-computer interaction. The key technologies of intelligent design for customized products include: a description and analysis of customer requirements (CRs), product family design (PFD) for the customer base, configuration and modular design for customized products, variant design for customized products, and a knowledge push for product intelligent design. The development trends in intelligent design for customized products include big-data-driven intelligent design technology for customized products and customized design tools and applications. The proposed method is verified by the design of precision computer numerical control (CNC) machine tools

Simplified algebraic estimation technique for sensor count reduction in single-phase converters with an active power buffer

Active pulsating power buffering (APPB) is an emerging technology that can effectively minimize the energy storage requirement of single-phase power conversion systems, potentially leading to high density and high reliability design. Nonetheless, the implementation of APPB generally requires the addition of excessive number of sensors in the circuit. Employing many sensors not only increases the system's volume and cost, but also undermines the system's robustness. Existing methods of reducing the sensor count in single-phase converters with an APPB suffer from issues such as high design complexity, noise sensitivity, and/or computational complexity. In this article, a simplified algebraic estimation technique is proposed to tackle the high sensor count problem. The proposed technique is intuitive to design and applicable to different topologies. It can effectively reduce the number of sensors while yielding similar or even better system's performance than that with a full set of sensors. Moreover, the technique features very low computational complexity, and can thus be easily implemented by low-cost microcontrollers. Experiments are conducted to verify the feasibilities of the proposed estimation and sensor reduction method. With this method, the sensor count can be reduced by 50%, while achieving a nearly 20-times computational time reduction as compared to that of the conventional method.This work was supported in part by the Hong Kong Research Grant Council under GRF Project 17205817 and in part by the Australian Research Council under DECRA Project DE210100473

Reconstruction of beagle hemi-mandibular defects with allogenic mandibular scaffolds and autologous mesenchymal stem cells.

Massive bone allografts are frequently used in orthopedic reconstructive surgery, but carry a high failure rate of approximately 25%. We tested whether treatment of graft with mesenchymal stem cells (MSCs) can increase the integration of massive allografts (hemi-mandible) in a large animal model.Thirty beagle dogs received surgical left-sided hemi-mandibular defects, and then divided into two equal groups. Bony defects of the control group were reconstructed using allografts only. Those of the experimental group were reconstructed using allogenic mandibular scaffold-loaded autologous MSCs. Beagles from each group were killed at 4 (n = 4), 12 (n = 4), 24 (n = 4) or 48 weeks (n = 3) postoperatively. CT and micro-CT scans, histological analyses and the bone mineral density (BMD) of transplants were used to evaluate defect reconstruction outcomes.Gross and CT examinations showed that the autologous bone grafts had healed in both groups. At 48 weeks, the allogenic mandibular scaffolds of the experimental group had been completely replaced by new bone, which has a smaller surface area to that of the original allogenic scaffold, whereas the scaffold in control dogs remained the same size as the original allogenic scaffold throughout. At 12 weeks, the BMD of the experimental group was significantly higher than the control group (p<0.05), and all micro-architectural parameters were significantly different between groups (p<0.05). Histological analyses showed almost all transplanted allogeneic bone was replaced by new bone, principally fibrous ossification, in the experimental group, which differed from the control group where little new bone formed.Our study demonstrated the feasibility of MSC-loaded allogenic mandibular scaffolds for the reconstruction of hemi-mandibular defects. Further studies are needed to test whether these results can be surpassed by the use of allogenic mandibular scaffolds loaded with a combination of MSCs and osteoinductive growth factors

An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality

The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in the delay time series, some studies have used Granger causality and transfer entropy to explore whether there is a causal relationship between any two airports. However, no research has yet established a delay causal network from the perspective of the airport network as a whole. To this end, an attention mechanism is introduced into the deep convolutional network architecture, and a deep temporal convolution prediction model considering the attention mechanism is proposed, so as to establish the relationship between different airport delay time series under the same network architecture. According to the attention factor score, the delay propagation causality between airports is preliminarily screened, and the direct causality is verified based on a t-test and propagation delay analysis. Taking China’s civil airport network as an example, the method proposed in this paper can not only discover the causal relationship of delays between airports but also characterize the strength of the relationship. Further analysis found that each airport is affected by an average of six airports, and airports with small delays are more likely to be affected by other airports

An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality

The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in the delay time series, some studies have used Granger causality and transfer entropy to explore whether there is a causal relationship between any two airports. However, no research has yet established a delay causal network from the perspective of the airport network as a whole. To this end, an attention mechanism is introduced into the deep convolutional network architecture, and a deep temporal convolution prediction model considering the attention mechanism is proposed, so as to establish the relationship between different airport delay time series under the same network architecture. According to the attention factor score, the delay propagation causality between airports is preliminarily screened, and the direct causality is verified based on a t-test and propagation delay analysis. Taking China&rsquo;s civil airport network as an example, the method proposed in this paper can not only discover the causal relationship of delays between airports but also characterize the strength of the relationship. Further analysis found that each airport is affected by an average of six airports, and airports with small delays are more likely to be affected by other airports