Designing vehicle exteriors requires repeated refinement of key proportions and viewpoints, a process traditionally reliant on manual sketching, which is often time-consuming and inefficient in early concept stages. To accelerate the design process, we are exploring the potential of utilizing AI for ideation in these early stages. However, it remains a challenging task to control proportions and maintain a fixed perspective when generating images using AI. To address these limitations, we present RatioMorph, a controllable image generation system that enables manipulation of vehicle proportions and viewpoints when generating images by AI. RatioMorph comprises two core modules. Car2BoxNet is a depth estimation model that transforms real photographs into structured box-style depth maps that capture the geometric layout of the vehicle. Box2CarNet is a diffusion-based image generator fine-tuned to produce vehicle designs that adhere to the provided geometric conditions. Both Car2BoxNet and Box2CarNet are trained on a synthetic dataset curated through automated filtering based on geometric alignment and visual quality. Evaluated within a production-adjacent automotive design workflow, RatioMorph significantly reduced early-stage design iteration time and enabled exploratory workflows that were difficult with previous AI workflows. This work introduces a domain-specific, controllable diffusion-based generation system tailored for automotive design, enabling manipulation of vehicle viewpoint and proportion. It demonstrates strong potential to accelerate early-stage workflows and outlines a path toward industrial deployment, with phased integration into production environments currently underway.

Edge devices such as wearables, drones, and CCTV systems have been widely deployed to collect real-world data, playing a crucial role in enhancing and securing urban life. However, these devices often struggle with significant performance challenges due to their limited computational and storage capacities when processing data locally. Offloading computation and data to the public cloud is straightforward but introduces high costs and latency. Alternatively, relying on an edge server to support a diverse array of heterogeneous edge devices can standardize operations. Still, it may lead to underutilization of high-performance devices such as Jetson Xavier if all tasks are centralized on the server. To address these concerns, we introduce ERPF, an edge resource provisioner that virtually extends edge devices' computation and storage capabilities, enabling them to handle complex tasks beyond their capacity. ERPF supports dynamic volume provisioning, GPU provisioning, and online execution context migration. Also, we propose a novel technique (ATS) that schedules AI workloads on distributed edge devices and edge servers with adjustment of task partition sizes based on the computational and network performance of the edge devices. ATS is seamlessly integrated into the ERPF prototype, which is implemented on a Kubernetes cluster using the Rook-Ceph storage orchestrator. Experimental results show that ERPF efficiently scales resources for edge devices through strategic offloading, while ATS delivers a substantial performance improvement of up to 23 × compared to baseline methods.

With the growing adoption of Internet of Things (IoT)sensors and applications, the volume of time-series data is expanding rapidly, underscoring the increasing importance of time-series databases (TSDBs) for efficient data management across edge and cloud environments. TSDBs are required to deliver high throughput and low latency to meet performance demands. However, due to strict data management rules, conventional TSDBs often overwrite redundant time-series data with identical timestamps. To overcome this limitation, we propose Tag-based Sequential Grouping (TSG), a novel approach that preserves time-series data with identical timestamps in TSDBs. TSG leverages tag identifiers as a secondary indexing mechanism to store data without overwriting, ensuring complete retention. We evaluate TSG on state-of-the-art TSDBs using real-world datasets. Our experimental results demonstrate that TSG successfully retains 100% of time-series data with identical timestamps across various TSDBs without data loss. Moreover, TSG significantly enhances read latency, outperforming existing methods by up to 322 times on a 6-hour interval range query.

Internet of Things (IoT) (or edge) devices are typically resource-constrained in terms of CPU, memory, and storage. Thus, it is viable for the devices to request resource provisioning to an edge server in the presence of growing data and heavy computation, as the edge server provides better accessibility than cloud servers. Consequently, the edge devices often perform computation and storage provisioning to the edge servers in large-scale data operations. However, the conventional methods for provisioning edge devices take into little consideration the characteristics of resources that jobs executed at the devices rely on. In particular, fully migrating computation jobs from the device to the server may waste valuable resources of the server without considering the computation and I/O characteristics of the jobs, thereby making the devices' resources idle. To overcome these limitations, we propose a novel Kubernetes-based resource augmentation framework, termed K-RAF, for provisioning edge devices with limited capabilities and accelerating the devices' job processing. Our experiment demonstrates that utilizing GPU acceleration, on average, K-RAF can run tasks 306 times faster than local computation on an edge device. Also, we show that utilizing the task distribution between an edge device and K-RAF can offer an average speedup of about 40% compared to K-RAF alone.

The purpose of this study is to develop the convergence study program through the educational robot to improve elementary students’ problem solving ability and have more confidence about mathematics, science, engineering and computer programming. Nowadays, convergence is a global trend, also the world has been trying to educate the person who adapted well to the fusion circumstances. However, the great majority of curriculum does not reflect this turn that carried new pedagogical trend. Thus, in order to overcome this situation, by making use of convergence study program, students will be easily adapted to the converging circumstances. It is expected to include coverage of resources for teaching of convergence study program through robot - education for instructors who may wish to adapt this new trend of instruction.