• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.775-783
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• 2017

As interest in cloud computing grows, the number of users using cloud computing services is increasing. However, cloud computing technology has been steadily challenged by security concerns. Therefore, various security breaches are springing up to enhance the system security for cloud services users. In particular, research on detection of malicious VM (Virtual Machine) is actively underway through the introspecting virtual machines on the cloud platform. However, memory analysis technology is not used as a monitoring tool in the environments where multiple virtual machines are run on a single server platform due to obstructive monitoring overhead. As a remedy to the challenging issue, we proposes a computationally efficient instance memory introspection scheme to minimize the overhead that occurs in memory dump and monitor it through a partial memory monitoring based on the well-defined kernel memory map library.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.4
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• pp.260-273
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• 2007

The wireless sensor network (WSN) nodes are required to operate for several months with the limited system resource such as memory and power. The hardware platform of WSN has 128Kbyte program memory and 8Kbytes data memory. Also, WSN node is required to operate for several months with the two AA size batteries. The MAC, Network protocol, and small application must be operated in this WSN platform. We look around the problem of memory and power for WSN requirements. Then, we propose a new computing model of system software for WSN node. It is the Atomic Object Model (AOM) with Dual Priority Scheduling. For the verification of model, we design and implement IEEE 802.15.4 MAC protocol with the proposed model.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.657-657
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• 2013

In-situ observations of nano-scale behavior of nanomaterials are very important to understand onthe nano-scale phenomena associated with phase change, atomic movement, electrical or optical properties, and even reactions which take place in gas or liquid phases. We have developed on the in-situ experimental technologies of nano-materials (nano-cluster, nanowire, carbon nanotube, and graphene, et al.) and their interactions (percolation of metal nanoclusters, inter-diffusion, metal contacts and phase changes in nanowire devices, formation of solid nano-pores, melting behavior of isolated nano-metal in a nano-cup, et al.) by nano-discovery membrane platform [1-4]. Between two microelectrodes on a silicon nitride membrane platform, electrical percolations of metal nano-clusters are observed with nano-structures of deposited clusters. Their in-situ monitoring can make percolation devices of different conductance, nanoclusters based memory devices, and surface plasmonic enhancement devices, et al. As basic evidence on the phase change memory, phase change behaviors of nanowire devices are observed at a nano-scale.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.141-151
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1139-1144
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• 2008

In this paper, we propose an automatic generation method of transaction level(TL) model from algorithmic model to verify system specification fast and effectively using virtual platform. The TL virtual platform including structural properties such as timing, synchronization and real-time is one of the effective verification frameworks. However, whenever change system specification or HW/SW mapping, we must rebuild virtual platform and additional design/verification time is required. And the manual description is very time-consuming and error-prone process. To solve these problems, we build TL library which consists of basic components of virtual platform such as CPU, memory, timer. We developed a set of design/verification tools in order to generate a virtual platform automatically. Our tools generate a virtual platform which consists of embedded real-time operating system (RTOS) and hardware components from an algorithmic modeling. And for communication between HW and SW, memory map and device drivers are generated. The effectiveness of our proposed framework has been successfully verified with a Joint Photographic Expert Group (JPEG) and H.264 algorithm. We claim that our approach enables us to generate an application specific virtual platform $100x{\tims}1000x$ faster than manual designs. Also, we can refine an initial platform incrementally to find a better HW/SW mapping. Furthermore, application software can be concurrently designed and optimized as well as RTOS by the generated virtual platform

• The Journal of Korean Medicine
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• v.29 no.5
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• pp.67-76
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• 2008

Objectives : Amnesia is theloss or impairment of memory, caused by physical injury, disease, drugs, or emotional trauma. Recently, the average life span is increasing, while at the same time, the incidence of dementia-like diseases in conjunction with amnesia are also increasing. Therefore learning and memory are very important issues in modern society. Ancient Korean physicians used several herbs to treat dementia and these herbal effects were described in Korean herbal books. Among them are some reports on several cognitive-enhancing herbs which have since been shown to improve dementia in recent pharmacological studies, such as Panax ginseng; however, the facilitatory effects of many Korean cognitive-enhancing herbs on learning and memory are limited. Learning and memory are essential requirements for every living organism in order to cope with environmental demands; cholinergic systems are known to be involved in learning and memory. Methods : In this study, the effects of Acori graminei rhizoma (AGR, 石菖蒲) on learning and memory were investigated by Morris water maze, eight-arm radial maze, and the effects on the central cholinergic system of rats injected with scopolamine. Results : In the water maze, the experimental animals were trained to find a platform in a fixed position for 6 days and then received a 60 sec probe trial in which the platform was removed from the pool on the 7th day. In the eight-arm radial maze, the animals were tested four times per day for 6 days. Scopolamine impaired performance of the maze tests and reduced activity of acetylcholinesterase (AchE) in the hippocampus, which is a marker for the central cholinergic system. There were significant reversals from the scopolamine-induced deficits on learning and memory in these tests, through daily administrations of AGR (100 mg/kg, p.o.) over 14 consecutive days. These treatments also reduced the loss of cholinergic activity in the hippocampus induced by scopolamine. Conclusions : These results demonstrated that AGR ameliorated learning and memory deficits by affecting the central acetylcholine system.

• The Journal of Korean Medicine
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• v.23 no.2
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• pp.125-138
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• 2002

Learning and memory are essential requirements for every living organism in order to cope with environmental demands, and cholinergic systems are known to be involved in learning and memory. Scutellaria baicalensis (SB) and Gastrodia elata (GE) as a traditional Oriental medicine have been clinically used to treat or prevent memory deficits, including Alzheimer's disease. In the present study, we investigated the effects of SB and GE on learning and memory in the Morris water maze task and the central cholinergic system of the rats with excitotoxic medial septum lesions. In the water maze test, the animals were trained to find a platform at a fixed position over 6 days and then received a 60-s probe trial in which the platform was removed from the pool on the 7th day. Ibotenic lesion of the medial septum (MS) impaired their performance in the maze test (latency of acquisition test on the 3rd day, $27.6{\pm}$4.4 sec vs. $61.7{\pm}17.7$ sec; retention test, $7.9{\pm}1.3%$ vs. $5.7{\pm}1.0%$: sharn vs. ibotenic lesioned groups, respectively) and reduced choline acetyltransferase (ChAT) - immunoreactivity in the MS and the hippocarnpus, which is a marker for degeneration of the central cholinergic system (number of cells, $21.1{\pm}1.1$ vs. $13.2{\pm}1.3$: sham vs. ibotenic lesioned group). Daily administrations of SB (100mg/kg, p.o.) and GE (100mg/kg, p.o.) for 21 consecutive days produced significant reversals of ibotenic acid-induced deficit in learning and memory. These treatments also reduced the loss of cholinergic immunoreactivity in the MS and the hippocarnpus induced by ibotenic acid. These results demonstrated that SB and GE ameliorated learning and memory deficits through effects on the central nervous system, partly through effect on the acetylcholine system. Our studies suggest an evidence of SB and GE as treatment for Alzheimer's disease.

• Journal of Platform Technology
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• v.11 no.4
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• pp.71-79
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• 2023

In this paper, we analyze the characteristics of machine learning workloads and, based on them, propose a distributed in-memory caching technique to improve the performance of machine learning workloads. The core of machine learning workload is model training, and model training is a computationally intensive task. Performing machine learning workloads in a Kubernetes-based cloud environment in which the computing framework and storage are separated can effectively allocate resources, but delays can occur because IO must be performed through network communication. In this paper, we propose a distributed in-memory caching technique to improve the performance of machine learning workloads performed in such an environment. In particular, we propose a new method of precaching data required for machine learning workloads into the distributed in-memory cache by considering Kubflow pipelines, a Kubernetes-based machine learning pipeline management tool.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.151-156
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• 2015

Recently, an embedded flash memory has been widely used for the Internet of Things(IoT). Due to its nonvolatility, economical feasibility, stability, low power usage, and fast speed. With respect to power consumption, the embedded memory system must consider the most significant design factor. The objective of this research is to design high performance and low power NAND flash memory architecture including a dual buffer as a replacement for NOR flash. Simulation shows that the proposed NAND flash system can achieve better performance than a conventional NOR flash memory. Furthermore, the average memory access time of the proposed system is better that of other buffer systems with three times more space. The use of a small buffer results in a significant reduction in power consumption.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.10
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• pp.446-451
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• 2006

In this paper, we propose an implementation of JPEG2000 codec on the ARM9 Processor which includes independent memory management facility. The codec and memory management facility together can control the encoding and the decoding process effectively within available memory area. Embedded appliances like cellular phones have very limited internal memory which can't be expanded easily. However, they should provide various applications and services using restricted memory resources. The proposed codec with memory management can provide image quality that is identical to the original image on embedded platform. The implemented codec has no memory conflict with other applications. It shows that the proposed codec can manage memory resources efficiently.