With the emergence of large-language models (LLM) and generative AI, which require an enormous amount of model parameters, the required memory bandwidth and capacity for high-end systems is on an ...unprecedented increase. To meet this need, we present an extended version of the high-bandwidth memory-3 (HBM3 DRAM), HBM3E, which achieves a 1280GB/s bandwidth with a cube density of 48GB. New design schemes and features, such as all-around power-through-silicon via (TSV), a 6-phase read-data-strobe (RDQS) scheme, a byte-mapping swap scheme, and a voltage-drift compensator for write data strobe (WDQS), are implemented to achieve extended bandwidth and capacity with enhanced reliability. The overall architecture and specifications, such as bump map footprint, the number of channel and I/Os, and the operation voltage, are identical to the latest HBM3 1, 2; therefore, backward compatibility is provided, avoiding system modification.
Epoxy composites (ECs) are used as epoxy molding compound (EMC), which significantly protects the integrated circuits from the humid environment. One of the major limitations with ECs is their ...phenomena of moisture absorption in humid environments, which drastically reduces their mechanical properties and hinders them from gaining widespread industrial applications. The low cross-linking density of conventional epoxy composites that can lead to intrinsically providing hydrophilic sites and low activation energy in water molecule diffusion. Herein, a dual-networked epoxy composite system as a platform technique by adopting strategically designed poly (methyl methacrylate)-b-(dimethyl aminoethyl methacrylate) (PMD) as polymer compatibilizer to improve the water absorption resistance and mechanical properties derived from good interfacial adhesion and high cross-linking density of epoxy/silica composites were investigated. The as-prepared dual-networked epoxy composite system demonstrates a high-water absorption suppression of about 166.78% and a high tensile modulus of 4.6 GPa, compared to other epoxy composites. In addition, the current strategy has excellent expandability to various fillers also applicable to diverse epoxy composite systems.
•The dual-networked system is newly developed for the epoxy composite system.•Functionalized silica improves the cross-linking density in epoxy composites.•The water absorption of ECFS composites was significantly suppressed.•The acquired ECFS composites exhibit superior mechanical properties.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. ...Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named e X tremely large-scale M ulti-mod A l S ensor dataset ( X-MAS ) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms.
The lithium-ion battery is one of the most widely used secondary batteries in various fields because of its high energy density and good life characteristics. A battery system is designed in a ...serial-parallel combination for the appropriate energy and power performance required by the system. However, due to problems in the manufacturing process of the lithium-ion battery, the cell characteristics may be manufactured differently, which may lead to imbalance between battery cells. In particular, contact resistance inevitably occurs between the tab of the battery and the bus bar connected to the anode and the cathode of the battery. Such contact resistance is a factor that discharges the current applied to the battery and affects performance and safety. Therefore, research on the effect of the contact resistance is required, and this research analyzes a phenomenon that may occur when the contact resistance of the pouch type battery cell is different based on an experiment. As a result, it was confirmed that high temperature heat is generated at the connection part as well as instability of the current applied to the battery depending on the size of the contact resistance, which may affect performance and safety.
In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. ...Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.
