Efficiently Disassemble-and-Pack for Mechanism

In this paper, we present a disassemble-and-pack approach for a mechanism to seek a box which contains total mechanical parts with high space utilization. Its key feature is that mechanism contains not only geometric shapes but also internal motion structures which can be calculated to adjust geometric shapes of the mechanical parts. Our system consists of two steps: disassemble mechanical object into a group set and pack them within a box efficiently. The first step is to create a hierarchy of possible group set of parts which is generated by disconnecting the selected joints and adjust motion structures of parts in groups. The aim of this step is seeking total minimum volume of each group. The second step is to exploit the hierarchy based on breadth-first-search to obtain a group set. Every group in the set is inserted into specified box from maximum volume to minimum based on our packing strategy. Until an approximated result with satisfied efficiency is accepted, our approach finish exploiting the hierarchy.Comment: 2 pages, 2 figure

A Comparative Study of Mammalian Diversification Pattern

Although mammals have long been regarded as a successful radiation, the diversification pattern among the clades is still poorly known. Higher-level phylogenies are conflicting and comprehensive comparative analyses are still lacking. Using a recently published supermatrix encompassing nearly all extant mammalian families and a novel comparative likelihood approach (MEDUSA), the diversification pattern of mammalian groups was examined. Both order- and family-level phylogenetic analyses revealed the rapid radiation of Boreoeutheria and Euaustralidelphia in the early mammalian history. The observation of a diversification burst within Boreoeutheria at approximately 100 My supports the Long Fuse model in elucidating placental diversification progress, and the rapid radiation of Euaustralidelphia suggests an important role of biogeographic dispersal events in triggering early Australian marsupial rapid radiation. Diversification analyses based on family-level diversity tree revealed seven additional clades with exceptional diversification rate shifts, six of which represent accelerations in net diversification rate as compared to the background pattern. The shifts gave origin to the clades Muridae+Cricetidae, Bovidae+Moschidae+Cervidae, Simiiformes, Echimyidae, Odontoceti (excluding Physeteridae+Kogiidae+Platanistidae), Macropodidae, and Vespertilionidae. Moderate to high extinction rates from background and boreoeutherian diversification patterns indicate the important role of turnovers in shaping the heterogeneous taxonomic richness observed among extant mammalian groups. Furthermore, the present results emphasize the key role of extinction on erasing unusual diversification signals, and suggest that further studies are needed to clarify the historical radiation of some mammalian groups for which MEDUSA did not detect exceptional diversification rates

DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning

Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones. Existing literature have applied the data augmentation (DA) to alleviate the model forgetting, while the role of DA in OCI has not been well understood so far. In this paper, we theoretically show that augmented samples with lower correlation to the original data are more effective in preventing forgetting. However, aggressive augmentation may also reduce the consistency between data and corresponding labels, which motivates us to exploit proper DA to boost the OCI performance and prevent the CF problem. We propose the Enhanced Mixup (EnMix) method that mixes the augmented samples and their labels simultaneously, which is shown to enhance the sample diversity while maintaining strong consistency with corresponding labels. Further, to solve the class imbalance problem, we design an Adaptive Mixup (AdpMix) method to calibrate the decision boundaries by mixing samples from both old and new classes and dynamically adjusting the label mixing ratio. Our approach is demonstrated to be effective on several benchmark datasets through extensive experiments, and it is shown to be compatible with other replay-based techniques.Comment: 10 pages, 7 figures and 3 table

Collaborative Planning for Catching and Transporting Objects in Unstructured Environments

Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation.In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form.This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state.Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints.We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance.Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality

Enhanced variable reluctance energy harvesting for self-powered monitoring

With the rapid development of microelectronic technology, wireless sensor nodes have been widely used in rotational equipment for health condition monitoring. However, for many low-frequency applications, there still remains an open issue of harvesting sufficient electrical energy to provide long-term service. Therefore, in this paper an enhanced variable reluctance energy harvester (EVREH) is proposed for self-powered health monitoring under low-frequency rotation conditions. A periodic arrangement of magnets and teeth is employed to achieve frequency up-conversion for performance enhancement under a specific space constraint. In addition, the permeance of the air gap is calculated by the combined magnetic field division and substituting angle method, and the output model of the EVREH is derived for parametric analysis based on the law of electromagnetic induction. Simulations and experimental evaluations under a range of structural parameters are then carried out to verify the effectiveness of the proposed model and investigate the output performance of the proposed harvester. The experimental results indicate that the proposed energy harvester could produce a voltage of 8.7 V and a power of 726 mW for a rotational speed of 200 rpm, with a power density of 0.545 mW/(cm3∙Hz2). Moreover, a self-powered wireless sensing system based on the proposed energy harvester is demonstrated, obtaining a vibration spectrum of the rotating motor and stator which can determine the health state of the system during low rotational speeds. Therefore, this autonomous self-sensing experiment verifies the potential of the EVREH for self-powered monitoring in low-frequency rotation applications.</p

Rapid classification of micro-particles using multi-angle dynamic light scatting and machine learning approach

The rapid classification of micro-particles has a vast range of applications in biomedical sciences and technology. In the given study, a prototype has been developed for the rapid detection of particle size using multi-angle dynamic light scattering and a machine learning approach by applying a support vector machine. The device consisted of three major parts: a laser light, an assembly of twelve sensors, and a data acquisition system. The laser light with a wavelength of 660 nm was directed towards the prepared sample. The twelve different photosensors were arranged symmetrically surrounding the testing sample to acquire the scattered light. The position of the photosensor was based on the Mie scattering theory to detect the maximum light scattering. In this study, three different spherical microparticles with sizes of 1, 2, and 4 μm were analyzed for the classification. The real-time light scattering signals were collected from each sample for 30 min. The power spectrum feature was evaluated from the acquired waveforms, and then recursive feature elimination was utilized to filter the features with the highest correlation. The machine learning classifiers were trained using the features with optimum conditions and the classification accuracies were evaluated. The results showed higher classification accuracies of 94.41%, 94.20%, and 96.12% for the particle sizes of 1, 2, and 4 μm, respectively. The given method depicted an overall classification accuracy of 95.38%. The acquired results showed that the developed system can detect microparticles within the range of 1–4 μm, with detection limit of 0.025 mg/ml. Therefore, the current study validated the performance of the device, and the given technique can be further applied in clinical applications for the detection of microbial particles

XAIR: A Framework of Explainable AI in Augmented Reality

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.Comment: Proceedings of the 2023 CHI Conference on Human Factors in Computing System

Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations

The baiji, or Yangtze River dolphin (Lipotes vexillifer), is a flagship species for the conservation of aquatic animals and ecosystems in the Yangtze River of China; however, this species has now been recognized as functionally extinct. Here we report a high-quality draft genome and three re-sequenced genomes of L. vexillifer using Illumina short-read sequencing technology. Comparative genomic analyses reveal that cetaceans have a slow molecular clock and molecular adaptations to their aquatic lifestyle. We also find a significantly lower number of heterozygous single nucleotide polymorphisms in the baiji compared to all other mammalian genomes reported thus far. A reconstruction of the demographic history of the baiji indicates that a bottleneck occurred near the end of the last deglaciation, a time coinciding with a rapid decrease in temperature and the rise of eustatic sea level