• International journal of advanced smart convergence
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• v.12 no.1
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• pp.70-75
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• 2023

With the recent proliferation of memory-intensive workloads such as deep learning, analyzing memory access characteristics for efficient memory management is becoming increasingly important. Since read and write operations in memory access have different characteristics, an efficient memory management policy should take into accountthe characteristics of thesetwo operationsseparately. Although some previous studies have considered the different characteristics of reads and writes, they require a modified hardware architecture supporting read bits and write bits. Unlike previous approaches, we propose a software-based management policy under the existing memory architecture for considering read/write characteristics. The proposed policy logically partitions memory space into the read/write area and the write area by making use of reference bits and dirty bits provided in modern paging systems. Simulation experiments with memory access traces show that our approach performs better than the CLOCK algorithm by 23% on average, and the effect is similar to the previous policy with hardware support.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.37-45
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• 2006

In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.311-320
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• 2021

At present, the secondary school teacher employment examination(SSTEE) is the only gateway to become a national and public secondary teacher in Korea, and after the revision from the 2014 academic year, all the questions of the exam have been converted to supply-type test items, requiring more definitive, accurate, and solid answers. Compared to the selection-type test items that measure recognition memory, the supply-type questions, testing recall memory, require constant memorization and retrieval practices to furnish answers; however, there is not enough learning tools available to support the practices. At this juncture, this study invented a mobile app, called ONE PASS, for the SSTEE. By unpacking the functional mechanisms of the brain, the basis of cognitive processing, this ONE PASS app offers a set of tools that feature brain-based learning principles, such as a personalized study planner, motivation measurement scales, mind mapping, brainstorming, and sample questions from previous tests. This study is expected to contribute to the research on the development of learning contents for applications, and at the same time, it hopes to be of some help for candidates in their exam preparation process.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.2
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• pp.129-135
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• 2020

SHP2 is an unusual protein phosphatase that functions as an activator for several signaling pathways, including the RAS pathway, while most other phosphatases suppress their downstream signaling cascades. The physiological and pathophysiological roles of SHP2 have been extensively studied in the field of cancer research. Mutations in the PTPN11 gene which encodes SHP2 are also highly associated with developmental disorders, such as Noonan syndrome (NS), and cognitive deficits including learning disabilities are common among NS patients. However, the molecular and cellular mechanism by which SHP2 is involved in cognitive functions is not well understood. Recent studies using SHP2 mutant mice or pharmacological inhibitors have shown that SHP2 plays critical role in learning and memory and synaptic plasticity. Here, we review the recent studies demonstrating that SHP2 is involved in synaptic plasticity, and learning and memory, by the regulation of the expression and/or function of glutamate receptors. We suggest that each cell type may have distinct paths connecting the dots between SHP2 and glutamate receptors, and these paths may also change with aging.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.54-61
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• 2009

This paper presents a new electromyogram (EMG) pattern recognition method based on the Hierarchical Temporal Memory (HTM) algorithm which is originally devised for image pattern recognition. In the modified HTM algorithm, a simplified two-level structure with spatial pooler, temporal pooler, and supervised mapper is proposed for efficient learning and classification of the EMG signals. To enhance the recognition performance, the category information is utilized not only in the supervised mapper but also in the temporal pooler. The experimental results show that the ten kinds of hand motion are successfully recognized.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.90-94
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• 2020

Artificial intelligence (AI) is software that learns large amounts of data and provides the desired results for certain patterns. In other words, learning a large amount of data is very important, and the role of memory in terms of computing systems is important. Massive data means wider bandwidth, and the design of the memory system that can provide it becomes even more important. Providing wide bandwidth in AI systems is also related to power consumption. AlphaGo, for example, consumes 170 kW of power using 1202 CPUs and 176 GPUs. Since more than 50% of the consumption of memory is usually used by system chips, a lot of investment is being made in memory technology for AI chips. MRAM, PRAM, ReRAM and Hybrid RAM are mainly studied. This study presents various memory technologies that are being studied in artificial intelligence chip design. Especially, MRAM and PRAM are commerciallized for the next generation memory. They have two significant advantages that are ultra low power consumption and nearly zero leakage power. This paper describes a comparative analysis of the four representative new memory technologies.

• Journal of Microbiology and Biotechnology
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• v.23 no.12
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• pp.1779-1784
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• 2013

Although enzyme-hydrolyzed silk fibroin has been reported to enhance cognitive function before, it has been still unknown which peptides can improve memory. Here we report that amino acid sequences of three novel peptides were identified from fibroin hydrolysate. Fibroin hydrolysate was obtained by hydrolysis with protease after partial hydrolysis with 5M $CaCl_2$. Synthesized peptides derived from these sequences improved scopolamine-induced memory impairments in mice. We confirmed this hydrolysate had effects that improved learning and memory abilities by performing the Rey-Kim test. From this hydrolysate of silk fibroin, amino acid sequences of eight peptides were identified by LC-MS/MS. Three peptides (GAGAGTGSSGFGPY, GAGAGSGAGSGAGAGSGAGAGY, and SGAGSGAGAGSGAGAGSGA) were synthesized to investigate whether they could improve memory. Passive avoidance test and Morris water maze test were performed, and all peptides showed memory-enhancing abilities on scopolamine-induced memory impairments in mice. In this study, we identified three novel peptides that could improve memory, and that silk fibroin hydrolysate was a mixture of various active peptides that could enhance memory.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.47 no.3
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• pp.195-203
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• 2014

We investigated the effect low salt (2 or 4% salt) concentrations jeotgal made from Todarodes pacificus on the learning and memory impairments in scopolamine-induced (2 mg/kg, i.p.) dementia rats. Rats treated with oral BF-7 (200 mg/kg, p.o.) as a positive control and Todarodes pacificus jeotgal had significantly reduced scopolamine-induced memory deficits in the passive avoidance test. The Morris water maze test or treatment with 2% salt jeotgal made from Todarodes pacificus significantly ameliorated the scopolamine-induced memory deficits in the formation of long- and short-term memory. The acetylcholine content and acetylcholinesterase acitivity paralleled the results of the behavior experiment. There were no significant differences in the brain acetylcholine contents of the experimental groups, while the brain acetylcholine content of the group treated with 2% salt Todarodes pacificus jeotgal was higher than that of the control group. The inhibitory effect of 2% salt jeotgal made from Todarodes pacificus on the acetylcholinesterase activity in the brain was lower than that of the control group. These trends were similar to those of the gamma-aminobutyric acid content. We suggest that Todarodes pacificus jeotgal enhances learning memory and cognitive function by regulating cholinergic enzymes.

• Toxicological Research
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• v.24 no.3
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• pp.183-188
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• 2008

The behavioral phenotypes of out-bred ICR mice were compared with those of in-bred C57BL/6 and BALB/c mice. In particular, this study examined the locomotor activity and two forms of hippocampus-dependent learning paradigms, passive avoidance and object recognition memory. The basal open-field activity of the ICR strain was greater than that of the C57BL/6 and BALB/c strains. In the passive avoidance task, all the mice showed a significant increase in the cross-over latency when tested 24 hours after training. The strength of memory retention in the ICR mice was relatively weak and measurable, as indicated by the shorter cross-over latency than the C57BL/6 and BALB/c mice. In the object recognition memory test, all strains had a significant preference for the novel object during testing. The index for the preference of a novel object was lower for the ICR and BALB/c mice. Nevertheless, the variance and the standard deviation in these strains were comparable. Overall, these results confirm the strain differences on locomotor activity and hippocampus-dependent learning and memory in mice.