• Journal of Information Processing Systems
• /
• v.18 no.3
• /
• pp.332-343
• /
• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• International Journal of Computer Science & Network Security
• /
• v.23 no.5
• /
• pp.207-211
• /
• 2023

Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.9A
• /
• pp.1442-1450
• /
• 1999

In this paper, we present an efficient hardware architecture for the frame memory of the MPEG-2 video encoder. Both the total size of internal buffers and the number of logic gates are reduced by the proposed memory map which can provide an effective interface between MPEG-2 video encoder and the external DRAM. Furthermore, the proposed scheme can reduce the DRAM access time. To realize the frame memory hardware,$0.5\mu\textrm{m}$, VTI, vemn5a3 standard cell library is used. VHDL simulator and logic synthesis tool are used for hardware design and RTL (register transfer level) function verification. The frame memory hardware emulator of the proposed architecture is designed for gate-level function verification. It is expected that the proposed frame memory hardware using VHDL can achieve suitable performance for MPEG-2 MP@ML.

• BMB Reports
• /
• v.49 no.4
• /
• pp.199-200
• /
• 2016

Gene regulation in the brain is essential for long-term plasticity and memory formation. Despite this established notion, the quantitative translational map in the brain during memory formation has not been reported. To systematically probe the changes in protein synthesis during memory formation, our recent study exploited ribosome profiling using the mouse hippocampal tissues at multiple time points after a learning event. Analysis of the resulting database revealed novel types of gene regulation after learning. First, the translation of a group of genes was rapidly suppressed without change in mRNA levels. At later time points, the expression of another group of genes was downregulated through reduction in mRNA levels. This reduction was predicted to be downstream of inhibition of ESR1 (Estrogen Receptor 1) signaling. Overexpressing Nrsn1, one of the genes whose translation was suppressed, or activating ESR1 by injecting an agonist interfered with memory formation, suggesting the functional importance of these findings. Moreover, the translation of genes encoding the translational machineries was found to be suppressed, among other genes in the mouse hippocampus. Together, this unbiased approach has revealed previously unidentified characteristics of gene regulation in the brain and highlighted the importance of repressive controls.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.1
• /
• pp.24-32
• /
• 1998

The bus contention among bus-based shared-memory multiprocessors limits their performance. In addition, under split bus transaction environment, multiprocessors may make some memory requests unnecessary stand by in the memory access buffer, which makes system performance worse. This unnecessary stand-by can be eliminated by maintaining the bitmap table which contains the status bit for each memory block. However, this mechanism requires a great size of SRAM for the status information, which is fully mapped from the whole memory blocks. To solve this problem, we propose a bitmap cache which exploits partial mapping and locality of references. The simulation results show that the proposed system can greatly reduce the capacity of SRAM for the status information with little deteriorating its performance.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.408-415
• /
• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.

• The Journal of Korean Medicine
• /
• v.30 no.3
• /
• pp.1-14
• /
• 2009

Objectives : Alzheimer's disease (AD) is characterized by neuronal loss and extracellular senile plaque. Moreover, the cellular actions of ${\beta}$-amyloid (A${\beta}$ play a causative role in the pathogenesis of AD. This study was designed to determine whether Woo-Gui-Um, a commonly used Korean herbal medicine, has the ability to protect cortical and hippocampal neurons against A${\beta}_{25-35}$ neurotoxicity Methods : In the present study, the authors investigated the preventative effects of the water extract of Woo-Gui-Um in a mouse model of AD. Memory impairment was induced by intraventricularly (i.c.v.) injecting A${\beta}_{25-35}$ peptides into mice. Woo-Gui-Um extract was then administered orally (p.o.) for 14 days. In addition, A${\beta}_{25-35}$ toxicity on the hippocampus was assessed immunohistochemically, by staining for Tau, MAP2, TUNEL, and Bax, and by performing an in vitro study in PC12 cells. Results : Woo-Gui-Um extract had an effect to improve learning ability and memory score in the water maze task. Woo-Gui-Um extract had significant neuroprotective effects in vivo against oxidative damage and apoptotic cell death of hippocampal neurons caused by i.c.v. A${\beta}_{25-35}$. In addition, Woo-Gui-Um extract was found to have a protective effect on A${\beta}_{25-35}$-induced apoptosis, and to promote neurite outgrowth of nerve growth factor (NGF)-differentiated PC12 cells. Conclusions : These results suggest that Woo-Gui-Um extract reduces memory impairment and Alzheimer's dementia via an anti-apoptotic effect and by regulating Tau and MAP2 in the hippocampus.

• Journal of Internet of Things and Convergence
• /
• v.9 no.1
• /
• pp.71-76
• /
• 2023

Recently, in order to store and manage big data, research and development of a high-performance storage system capable of stably accessing large data have been actively conducted. In particular, storage systems in data centers and enterprise environments use large amounts of SSD (solid state disk) to manage large amounts of data. In general, SSD uses FTL(flash transfer layer) to hide the characteristics of NAND flash memory, which is a medium, and to efficiently manage data. However, FTL's algorithm has a limitation in using DRAM more to manage the location information of NAND where data is stored as the capacity of SSD increases. Therefore, this paper introduces FTL policies that apply virtual memory to reduce DRAM resources used in FTL. The virtual memory-based FTL policy proposed in this paper manages the map data by using LRU (least recently used) policy to load the mapping information of the recently used data into the DRAM space and store the previously used information in NAND. Finally, through experiments, performance and resource usage consumed during data write processing of virtual memory-based FTL and general FTL are measured and analyzed.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.11C
• /
• pp.1052-1061
• /
• 2003

This paper proposes a novel normalization scheme in the state metric calculation unit for the Block-wise MAP Turbo decoder. The proposed scheme subtracts one of four metrics from the state metrics in a trellis stage and shifts, if necessary, those metrics for normalization. The proposed architecture can reduce power consumption and memory requirement by reducing the number of the state metrics by one in a trellis stage in the Block-wise MAP decoder which requires an intensive state metric calculations. Simulation results show that dynamic power has been reduced by 17.9% and area has been reduced by 6.6% in the Turbo decoder employing the proposed normalization scheme, when compared to the conventional Block-wise MAP Turbo decoders.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.42 no.9 s.339
• /
• pp.73-80
• /
• 2005

The Turbo decoders based on Log-MAP decoding algorithm inherently requires large amount of memory and intensive complexity of hardware due to iterative decoding, despite of excellent decoding efficiency. To decrease the large amount of memory and reduce hardware complexity, the result of previous research. And this paper design the Turbo decoder applicable to the 3G W-CDMA systems. Through the result of previous research, we decided 5-bits for the received data 6-bits for a priori information, and 7-bits for the quantization state metrics. The error correction term for $MAX^{*}$ operation which is the main function of Log-MAP decoding algorithm is implemented with very small hardware overhead. The proposed Turbo decoder is synthesized in $0.35\mu$m Hynix CMOS technology. The synthesized result for the Turbo decoder shows that it supports a maximum 9Mbps data rate, and a BER of $10^{-6}$ is achieved(Eb/No=1.0dB, 5 iterations, and the interleaver size $\geq$ 2000).