We propose a novel and effective deep learning method, called deep pattern matching, for predicting the energy consumption of complex structures, which helps designers to develop ecological solutions with minimal fabrication energy for additive manufacturing. This new method does not necessitate the real energy consumption values of complex structures for training the prediction model, substantially reducing the cost of training data collection, which can be prohibitively expensive. This novel method exploits simple structures whose real energy consumption values are far cheaper to measure, by matching the similar infill pattern of complex structures from simple structures, and then approximating the energy value of the pattern in the complex structures by the matched one in training phase. This effective algorithm is designed dynamically for allowing us to match patterns with arbitrary shapes. We evaluate our deep pattern matching algorithm on various complex structures, where the highest total energy accuracy is up to 97.3%. The extensive empirical results confirm the effectiveness and robustness of the proposed method, exhibiting a great potential to advance the real usage of deep learning models for energy consumption prediction of complex structures in ecological additive manufacturing.

4D printing encodes transformability over time, which empowers users to create artifacts by on-demand deformation. The creative process of 4D printing shape-changing artifacts can be challenging because of its discontinuous fabrication steps, such as digital designing, specific path planning, automatic printing and manual triggering. We hypothesize that switching from typical 4D printing reliant on 3D printers to a more “handcrafted” method can allow users to understand and continuously reflect upon the artifact and its transformability. Towards this vision, we introduce 4Doodle, a hybrid craft approach that integrates unique deformation controllability and five techniques for freehand 4D printing, using a 3D pen. To tackle the shape-changing challenges of uncertain hands-on fabrication, we develop a mixed reality system to help novices master the manual skills of 4D printing. We also demonstrate a series of 4D printed artifacts with fully human intervention. Finally, our user study shows that 4Doodle lowers the skill-acquisition barrier associated with handcrafting 4D printed artifacts, and it has great potential for creative production and spatial ability.

Sepsis is a serious complication of an infection. Without quick treatment it can lead to organ failure and death. Early detection and treatment of sepsis can thus improve patient outcomes. Yet, their effectiveness often relies on awareness and acceptance of said procedures. In this work, we implement sepsis check based on a widely accepted guideline for sepsis recognition (Sepsis-3). Our implementation achieved F-score as high as 0.874. In addition to implementing the ruled-based approach to early sepsis detection, we use an existing data-driven transformer-based STraTS model [1] for time-series forecasting to support sepsis check and directly predicting sepsis label using 24-hour patient data in a fully data-driven setup. The advantage of time series forecasting is improved handling of missing data and the potential of applying the Sepsis-3 definition to unobserved forecast data. Additionally, we attempt to improve STraTS model by integrating a clinical text embedding module to enable multimodal learning. Both the original STraTS model and our refined STraTS+Text model perform good in both forecasting (masked MSE, mean squared error at approximately 5.24) and classification task (ROC-AUC, area under receiver operating characteristic curve at approximately 0.89).

Many studies have been conducted on single lane detection, but multi-lane detection is rarely addressed. The latter is more advantageous for applications such as autonomous navigation, unmanned vehicles, departure warning, and cruise control. In this paper, we propose a novel and robust multiple lane detection algorithm based on the road structure information, which contains five complementary constraints: length constraint, parallel constraint, distribution constraint, pair constraint and uniform width constraint. All the five constraints are incorporated into a Hough transform (HT) based unified framework to select lane candidates. Nearly 99% of the false alarm candidates in HT space can be removed. Moreover, a dynamic programming strategy is proposed to find the most rational solutions among the remaining candidates. This strategy can effectively deal with combination complexity and interferences introduced by multi-lane detection. Experimental results on the benchmark dataset and other collected data demonstrate that the proposed method can outperform the state-of-the-art approaches in both accuracy and efficiency.

This work constructs an integrated logistics and transportation system to simulate enterprise-wide freight movements and to study the interactive influences among, and the effects of highly developed information technologies on, logistic decision-making, freight-distribution patterns, and commercial vehicle operations. The integrated system is formulated as a comprehensive product supply-chain system that integrates the logistical, informational, and physical aspects of transportation networks and consists of three components, i.e., a logistic decision-making solver, a freight-traffic simulator, and a real-time information generator. The logistic decision-making solver is designed to determine the freight distribution among the components involved in the supply chain based on market demand, supply, and the associated transaction cost. The freight-traffic simulator simulates commercial vehicle operations in the transportation network based on logistic decisions with or without real-time traffic information. The real-time information generator generates real-time traffic conditions and real-time supply and demand information. Finally, a numerical example is presented to show how the freight-shipment distribution pattern evolves based on dynamic transportation costs collected from previous trips simulated under various scenarios.