The purpose of this study is to propose an efficient machine learning model based on five years of data for Seoul Grand Park in Republic of Korea, depending on the weather and day characteristics, and to increase its effectiveness as a strategic foundation for national theme park management and marketing. To this end, the AdaBoost model, which reflects the characteristics of the weather and the day of the week, was recently compared with the actual number of visitors and the predicted number of visitors to analyze the accuracy. The analysis showed 30 days of abnormal cases, and the overall annual distribution was found to show similar patterns. Abnormal cases required details of wind speed, average relative humidity, and fine dust concentration for weather information, and it was derived that more accurate predictions would be possible considering variables such as group visitors, new events, and unofficial holidays.

We present ARES, an automated framework for writing endurance tests on flash-based storage devices. Since flash-based storages such as solid-state drives and SD cards have a limited capacity for processing data write requests, it is important for manufacturers to accurately test and specify the maximum amount of data writes that their products are guaranteed to withstand. Unfortunately, however, writing such an endurance test is mostly conducted manually in practice, which is difficult, laborious, and sometimes inaccurate. To address this issue, we present ARES, a learning-based automated approach for generating endurance tests on flash-based storage devices. ARES is built on two ideas. First, we observe that the search space of endurance tests can be effectively reduced by devising abstract relative write patterns. Second, we use a learning algorithm based on genetic programming in order to find worse-case write patterns efficiently. The experimental results demonstrate that ARES is capable of successfully learning highquality write patterns. The performance of the learned write patterns is superior to that of the manual tests designed by human engineers in Samsung Electronics. Especially for 32GB USB, ARES identified a write pattern that is 26% more effective than the manually crafted write pattern that has been used until recently.

As DNNs improving state-of-the-art accuracy on many artificial intelligence (AI) applications such as computer vision processing for autonomous driving, the data processing bandwidth and power consumption between neural network accelerator and the off-chip memory are big challenge to enhance the compute performance metric TOPs/watt. To overcome the limited compute and energy resources in automobile environment, inferencing by PIM (Processing in Memory) or AiM (Accelerator in Memory) which deployed MAC(Multiply and Accumulation) units and activation function inside DRAM is one of the key solution by using multi bank parallelism and memory cell architecture. When memory technology equipped with analog logic inside mature in the near future, ultra-low power analog accelerator based neuromorphic computing architecture will lead the future autonomous driving solution.

This study was conducted as a planning stage for development of wearable devices capable of managing the thermal diseases by applying the ICT(Information Communication Technology) in an endeavor to meet the urgent needs for countermeasures amid rapid increase in the number of patients with the thermal diseases caused as a result of global warming. The purpose of this study was to provide the basic data for development of wearable devices allowing the patients to be transported expeditiously to hospitals based on synchronization with medical institutions or enabling the prevention of diseases through the response system for each stage according to the reference values based on the data reflecting physical characteristics of individuals by applying the ICT, so that the thermal diseases can be managed effectively. For that, basic study will be conducted on expanding the role of the devices capable of protecting human lives from various thermal diseases caused by the scorching heat waves, which are affecting countries worldwide and expected to persist in the period ahead, by setting the goals of each stage for the thermal disease management platform and collecting necessary information. Based on the accumulated data, the functions of precise diagnosis and treatment can be expected through more accurate evidences pertaining to the thermal diseases.