• KIPS Transactions on Computer and Communication Systems
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• v.4 no.2
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• pp.41-46
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• 2015

The demand on low power and high performance system is soaring due to the extending of mobile and small electronic device market. The 3D die-stacking technology is widely studying for next generation integration technology due to its high density and low access time. We proposed the 3D die-stacked DRAM including a reconfigurable accelerator in a logic layer of DRAM. Also we discuss and suggest a cache-based local memory for a reconfigurable accelerator in a logic layer. The reconfigurable accelerator in logic layer of 3D die-stacked DRAM reduces the overhead of data management and transfer due to the characteristics of its location, so that can increase the performance highly. The proposed system archives 24.8 speedup in maximum.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.6
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• pp.448-453
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• 2019

This work reports the electrical characteristics of and temperature distribution in chalcogenide phase change memory (PCM) devices that have a self-aligned structure. GST (Ge-Sb-Te) chalcogenide alloy films were formed in a self-aligned manner by interdiffusion between sputter-deposited Ge and $Sb_2Te_3$ films during thermal annealing. A transmission electron microscopy-energy dispersive X-ray spectroscopy (TEM-EDS) analysis demonstrated that the local composition of the GST alloy differed significantly and that a $Ge_2Sb_2Te_5$ intermediate layer was formed near the $Ge/Sb_2Te_3$ interface. The programming current and threshold switching voltage of the PCM device were much smaller than those of a control device; this implies that a phase transition occurred only in the $Ge_2Sb_2Te_5$ intermediate layer and not in the entire thickness of the GST alloy. It was confirmed by computer simulation, that the localized phase transition and heat loss suppression of the GST alloy promoted a temperature rise in the PCM device.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.818-833
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• 2021

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model's F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.

• ETRI Journal
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• v.44 no.6
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• pp.1034-1046
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• 2022

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.

• Steel and Composite Structures
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• v.48 no.6
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• pp.611-623
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• 2023

This work considered an optimal control formulation in the sense of Caputo derivatives. The optimality of the fractional optimal control problem. The tumor immune interaction in fractional form provides an excellent tool for the description of memory and hereditary properties of inter and intra cells. So the interaction between effector-cells, tumor cells and are modeled by using the definition of Caputo fractional order derivative that provides the system with long-time memory and gives extra degree of freedom. In addiltion, existence and local stability of fixed points are investigated for discrete model. Moreover, in order to achieve more efficient computational results of fractional-order system, a discretization process is performed to obtain its discrete counterpart. Our technique likewise allows the advancement of results, such as return time to baseline that are unrealistic with current model solvers.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.839-849
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• 2023

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.

• Earthquakes and Structures
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• v.27 no.1
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• pp.57-67
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• 2024

To assess the seismic performance of Plate-Shell Integrated Concrete Liquid-Storage Structure (PSICLSS), a scaled test model was constructed. This model incorporated a hybrid isolation system, which combined shape memory alloy (SMA), lead-cored rubber isolation bearing (LRB) and sliding isolation bearing (SB). By conducting shaking table test, the dynamic responses of both non-isolated and hybrid-isolated PSICLSS were analyzed. The results show that the hybrid isolation system can effectively reduce the acceleration and displacement responses of the structure. However, it also results in an increase in local hydrodynamic pressure and liquid sloshing height. Under extreme earthquake action, the displacement of isolation layer is small. When vertical ground motion is taken into account, the shock absorption rate of horizontal acceleration decreases. The peak hydrodynamic pressure increases significantly, and the peak hydrodynamic pressure position also changes. The maximum displacement of isolation layer increases, the residual displacement decreases.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.583-594
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• 2006

The reliability concept of wireless sensor network is essential to get exactly actual data from the ubiquitous environment. A rollback technique for the self-healing helps to increase it. However, a fault can occur in wireless sensor network when to use a previous rollback technique because it is designed just for the local system. So, checkpoint protocols are suggested in order to use a rollback technique in the network without the fault. However, there is trade-off among performance overhead, power consumption, and memory overhead for each of protocols. Hence, we suggest a novel global checkpoint protocol, so called address log based protocol(ALBP), based on an asynchronous protocol. It is a platform based protocol to reduce power consumption, performance overhead, and memory overhead which are the most of consideration in wireless sensor network.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.111-120
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• 2011

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.2164-2170
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• 2010

Digital Radio Frequency Memory, ( as DRFM ), is a device with the ability to restore output to the input RF signal in the required time after storing the incoming RF signals. Therefore DRFM is widely used in Jammer, EW Simulator, Target Echo Generator, and so on. This paper proposes its hardware implementation composed with the high frequency part and the digital processing part consisting of RF input/output module and local oscillator module. It is also proposed the replicated signal generation method which is consisted of the Analog-Digital conversion in the form of pulsed RF signal quantization, and FPGA to save and produce the playback signal, and RF signals to produce a Digital-Analog Conversion in the digital processing unit. This proposed scheme applied to test board and confirmed the validity of the proposed scheme through the test results obtained by the simulated input signals.