• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.200-207
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• 2020

Recently, deep neural networks such as CNN are showing excellent performance in various fields such as image classification, object recognition, visual quality enhancement, etc. However, as the model size and computational complexity of deep learning models for most applications increases, it is hard to apply neural networks to IoT and mobile environments. Therefore, neural network compression algorithms for reducing the model size while keeping the performance have been being studied. In this paper, we apply few compression methods to CNN models and evaluate their performances in the embedded environment. For evaluate the performance, the classification performance and inference time of the original CNN models and the compressed CNN models on the image inputted by the camera are evaluated in the embedded board equipped with QCS605, which is a customized AI chip. In this paper, a few CNN models of MobileNetV2, ResNet50, and VGG-16 are compressed by applying the methods of pruning and matrix decomposition. The experimental results show that the compressed models give not only the model size reduction of 1.3~11.2 times at a classification performance loss of less than 2% compared to the original model, but also the inference time reduction of 1.2~2.21 times, and the memory reduction of 1.2~3.8 times in the embedded board.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Journal of Oriental Neuropsychiatry
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• v.10 no.1
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• pp.17-38
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• 1999

The effects of Sahyangsohapwon on the affective reactivity of rats were studied with open-field behavior. Sample group was treated with the medicine for 8 weeks, whereas control group was treated with the vehicle. The effects of Sahyangsohapwon on the enhancement of learning and memory of AD model rats were studied with two-way avoidance task. Sample group electrically lesioned on nbM(nucleus basalis of Meynert) was treated with the medicine for 8 weeks, whereas control group with nbM lesion and sham group with the sham operation were treated with the vehicle. 1. In the open-field behavior task, the start latency from start box was measured $27.08{\pm}7.51sec$ in control group, $23.15{\pm}5.98sec$ in sample group. Rats in sample group showed a tendency of shortened latency going out to a strange place compared with those in control group, but with no statistical significance(p>0.05). 2. In the open-field behavior task, the number of locomotion crossing the grid lines was measured $84.54{\pm}3.55$ in control group, $116.93{\pm}6.41$ in sample group. There was an increased locomotion in sample group compared with control group with statistical significance(p<0.01). This can be interpreted as rats in sample group showed lowerd anxiety under a strange environment. 3. In the open-field behavior task, the rearing number was measured $7.46{\pm}0.57$ in control group, $10.13{\pm}0.95$ in sample group. There was an increased rearing in sample group compared with control group with statistical significance(p<0.05). This can also be interpreted as rats in sample group showed lowerd anxiety under a strange environment. 4. In the open-field behavior task, the number of crossing behavior was measured $5.54{\pm}1.50$ in control group, $9.20{\pm}1.67$ in sample group. There was a increasing tendency of crossing behavior in sample group compared with control group, but with no statistical significance(p<0.05). 5. In the open-field behavior task, the total activity was measured $97.54{\pm}4.70$ in control group, $136.27{\pm}792$ in sample group. There was an increased total activity in sample group compared with control group with statistical significance(p<0.01). This can also be interpreted as rats in sample group showed lowerd anxiety under a strange environment. 6. In the analysis of effects on the learning and memory in AD model rats with two-way avoidance task, the response latency was measured $6717{\pm}134msec$ in the 1st session, $5416{\pm}160msec$ in the 2nd session, $5252{\pm}148msec$ in the 3rd session in control group. It was measured $6724{\pm}155msec$ in the 1st session, $4642{\pm}139msec$ in the 2nd session, $4914{\pm}148msec$ in the 3rd session in sample group and $4357{\pm}144msec$ in the 1st session, $3125{\pm}115msec$ in the 2nd session, $3091{\pm}98msec$ in the 3rd session in sham group. There were differences between sham group and nbM lesioned groups with statistical significance in post hoc analysis(p<0.000). And in the 2nd session, there was a reduction of latency in sample group compared with control group with statistical significance (p<0.000). This showed that sample group had better learning capacity than control group. 7. In the analysis of effects on the learning and memory in AD model rats with two-way avoidance task, the number of avoidance response was measured $5.85{\pm}1.41$ in the 1st session, $14.23{\pm}2.89$ in the 2nd session, $15.69{\pm}2.56$ in the 3rd session in control group. It was measured $7.92{\pm}1.94$ in the 1st session, $16.83{\pm}2.29$ in the 2nd session, $15.42{\pm}2.81$ in the 3rd session in sample group and $14.38{\pm}1.62$ in the 1st session, $22.88{\pm}0.89$ in the 2nd session, $23.88{\pm}1.64$ in the 3rd session in sham group. There were differences between sham group and nbM lesioned groups with statistical significance in post hoc analysis(p<0.001). But between control and sample group, there was no significant difference. With the experimental results above, Sahyangsohapwon can be supposed to have the enhancing effects on the affect reactivity and learning with memory of AD model rats induced by electrolyte injury of nbM.

• Molecules and Cells
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• v.38 no.9
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• pp.796-805
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• 2015

Gintonin is a novel ginseng-derived lysophosphatidic acid (LPA) receptor ligand. Oral administration of gintonin ameliorates learning and memory dysfunctions in Alzheimer's disease (AD) animal models. The brain cholinergic system plays a key role in cognitive functions. The brains of AD patients show a reduction in acetylcholine concentration caused by cholinergic system impairments. However, little is known about the role of LPA in the cholinergic system. In this study, we used gintonin to investigate the effect of LPA receptor activation on the cholinergic system in vitro and in vivo using wild-type and AD animal models. Gintonin induced $[Ca^{2+}]_i $ transient in cultured mouse hippocampal neural progenitor cells (NPCs). Gintonin-mediated $[Ca^{2+}]_i $ transients were linked to stimulation of acetylcholine release through LPA receptor activation. Oral administration of gintonin-enriched fraction (25, 50, or 100 mg/kg, 3 weeks) significantly attenuated scopolamine-induced memory impairment. Oral administration of gintonin (25 or 50 mg/kg, 1 2 weeks) also significantly attenuated amyloid-${\beta}$ protein ($A{\beta}$)-induced cholinergic dysfunctions, such as decreased acetylcholine concentration, decreased choline acetyltransferase (ChAT) activity and immunoreactivity, and increased acetylcholine esterase (AChE) activity. In a transgenic AD mouse model, long-term oral administration of gintonin (25 or 50 mg/kg, 3 months) also attenuated AD-related cholinergic impairments. In this study, we showed that activation of G protein-coupled LPA receptors by gintonin is coupled to the regulation of cholinergic functions. Furthermore, this study showed that gintonin could be a novel agent for the restoration of cholinergic system damages due to $A{\beta}$ and could be utilized for AD prevention or therapy.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.7 s.337
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• pp.27-34
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• 2005

Iterative detection (ID) has proven to be a near-optimal solution for concatenated Finite State Machines (FSMs) with interleavers over an additive white Gaussian noise (AWGN) channel. When perfect channel state information (CSI) is not available at the receiver, an adaptive ID (AID) scheme is required to deal with the unknown, and possibly time-varying parameters. The basic building block for ID or AID is the soft-input soft-output (SISO) or adaptive SISO (A-SISO) module. In this paper, Reduced State SISO (RS-SISO) algorithms have been applied for complexity reduction of the A-SISO module. We show that serially concatenated CPM (SCCPM) with AID has turbo-like performance over fading ISI channels and also RS-A-SISO systems have large iteration gains. Various design options for RS-A-SISO algorithms are evaluated. Recently developed density evolution technique is used to analyze RS-A-SISO algorithms. We show that density evolution technique that is usually used for AWGN systems is also a good analysis tool for RS-A-SISO systems over frequency-selective fading channels.

• The Journal of Korean Medicine
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• v.35 no.4
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• pp.10-23
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• 2014

Objectives: This study evaluated the effects of Guibi-tang (GBT) on neuronal apoptosis and cognitive impairment induced by beta amyloid ($A{\beta}$), (1-42) injection in the hippocampus of ICR mice. Methods: $A{\beta}$ (1-42) was injected unilaterally into the lateral ventricle using a Hamilton syringe and micropump ($2{\mu}g/3{\mu}{\ell}$, $0.6{\mu}{\ell}/min$). Water extract of GBT was administered orally once a day (500 mg/kg) for 3 weeks after the $A{\beta}$ (1-42) injection. Acquisition of learning and retention of memory were tested using the Morris water maze. Neuronal damage and $A{\beta}$ accumulation in the hippocampus was observed using cresyl violet and Congo red staining. The anti-apoptotic effect of GBT was evaluated using TUNEL labeling in the hippocampus. Results: GBT significantly shortened the escape latencies during acquisition training trials. GBT significantly increased the number of target headings to the platform site, the swimming time spent in the target quadrant, and significantly shortened the time for the 1st target heading during the retention test trial. GBT significantly attenuated the reduction in thickness and number of CA1 neurons, and $A{\beta}$ accumulation in the hippocampus produced by $A{\beta}$ (1-42) injection. GBT significantly reduced the number of TUNEL-labeled neurons in the hippocampus. Conclusion: These results suggest that GBT improved cognitive impairment by reducing neuronal apoptosis and $A{\beta}$ accumulation in the hippocampus. GBT may be a beneficial herbal formulation in treating cognitive impairment including Alzheimer's disease.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.3 s.345
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• pp.48-58
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• 2006

This paper presents 8x8 two dimensional DCT/IDCT processor of adder-based distributed arithmetic architecture without applying ROM units in conventional memories. To reduce hardware cost in the coefficient matrix of DCT and IDCT, an odd part of the coefficient matrix was shared. The proposed architecture uses only 29 adders to compute coefficient operation in the 2-D DCT/IDCT processor, while 1-D DCT processor consists of 18 adders to compute coefficient operation. This architecture reduced 48.6% more than the number of adders in 8x8 1-D DCT NEDA architecture. Also, this paper proposed a form of new transpose network which is different from the conventional transpose memory block. The proposed transpose network block uses 64 registers with reduction of 18% more than the number of transistors in conventional memory architecture. Also, to improve throughput, eight input data receive eight pixels in every clock cycle and accordingly eight pixels are produced at the outputs.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.1
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• pp.33-46
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• 2003

A Home Location Register(HLR) database system manages each subscriber's location information, which continuously changes in a cellular network. For this purpose, the HLR database system provides table management, index management, and backup management facilities. In this thesis, we propose using a two-level index method for the mobile directory number(MDN) as a suitable method and a chained bucket hashing method for the electronic serial number(ESN). Both the MDN and the ESN are used as keys in the HLR database system. We also propose an efficient backup method that takes into account the characteristics of HLR database transactions. The retrieval speed and the memory usage of the two-level index method are better than those of the R-tree index method. The insertion and deletion overhead of the chained bucket hashing method is less than that of the modified linear hashing method. In the proposed backup method, we use two kinds of dirty flags in order to solve the performance degradation problem caused by frequent registration-location operations. For a million subscribers, proposed techniques support reduction of memory size(more than 62%), directory operations (2500,000 times), and backup operations(more than 80%) compared with current techniques.