• STI Policy Review
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• v.1 no.1
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• pp.93-109
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• 2010

Over the last decade, some Korean enterprises have emerged to become global players in their specialized products. How have they achieved such tremendous technological progress in a short period of time? This paper explores that question by examining the characteristics of technological innovation activities at major Korean enterprises. The paper begins with a brief review of the stages of economic growth and science and technology development in Korea. Then, the existing literature, explaining the Korean innovation model, is analyzed in order to establish a new framework for the Korean innovation model. Specifically, Korean firms have experienced three sequential phases, and thus, the Korean model, at the firm level, can be coined as "path-following," "path-revealing," and "path-creating." Then, the stylized facts in the first phase (path-following) and the second phase (path-revealing) are discussed, in the context of empirical evidence from the areas of memory chips, automobiles, shipbuilding, and steel. In terms of technology development, the Korean model has evolved as "collective learning" in the first phase, "collective recombination" of existing knowledge and technology in the second phase, and is assumed as "collective creativity" in the third phase. Ultimately, all three can be classified as "collective creation". Korean firms now face a transition in the modes of technological innovation in order to efficiently implement the third phase. To achieve remarkable progress again, as they did in the past, and to sustain the growth momentum, Korean firms should challenge new dimensions such as creative technological ideas, distinctive technological capabilities, and unique innovation systems -- all of which connote 'uniqueness'. Finally, some lessons from the Korean technological innovation experience are addressed.

• Journal of Ginseng Research
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• v.30 no.2
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• pp.57-63
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• 2006

One of the various signaling agents in the animal cells is the simple ion called calcium, $Ca^{2+}$.$Ca^{2+}$ controls almost everything that animals do, including fertilization, secretion, metabolism, muscle contractions, heartbeat, learning, memory stores, and more. To do all of this, $Ca^{2+}$ acts as an intracellular messenger, relaying information within cells to regulate their activity. In contrast, the maintenance of intracellular high $Ca^{2+}$ concentrations caused by various excitatory agents or toxins can lead to the disintegration of cells (necrosis) through the activity of $Ca^{2+}$-sensitive protein-digesting enzymes. High concentrations of calcium have also been implicated in the more orderly programs of cell death known as apoptosis. Because this simple ion, acts as an agent for cell birth, life and death, to coordinate all of these functions, $Ca^{2+}$ signalings should be regulated precisely and tightly. Recent reports have shown that ginsenosides regulate directly and indirectly intracellular $Ca^{2+}$ level with differential manners between neuronal and non-neuronal cells. This brief review will attempt to survey how ginsenosides differentially regulate intracellular $Ca^{2+}$ signaling mediated by various ion channels and receptor activations in neuronal and non-neuronal cells.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.20-24
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• 2021

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.62-74
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• 2023

In response to diverse external stimuli, sensory receptors generate spiking nerve signals. These generated signals are transmitted to the brain along the neural pathway to advance to the stage of recognition or perception, and then they reach the area of discrimination or judgment for remembering, assessing, and processing incoming information. We review research trends in neuromorphic sensory perception technology inspired by biological sensory perception functions. Among the various senses, we consider sensory nerve decoding technology based on sensory nerve pathways focusing on touch and smell, neuromorphic synapse elements that mimic biological neurons and synapses, and neuromorphic processors. Neuromorphic sensory devices, neuromorphic synapses, and artificial sensory memory devices that integrate storage components are being actively studied. However, various problems remain to be solved, such as learning methods to implement cognitive functions beyond simple detection. Considering applications such as virtual reality, medical welfare, neuroscience, and cranial nerve interfaces, neuromorphic sensory recognition technology is expected to be actively developed based on new technologies, including combinatorial neurocognitive cell technology.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Journal of Ginseng Research
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• v.31 no.3
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• pp.117-126
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• 2007

Ginseng, the root of Panax species, a well-known herbal medicine has been used as a traditional medicine for thousands of years and is now a popular and worldwide used natural medicine. Ginseng has been used primarily as a tonic to invigorate weak bodies to help the restoration of homeostasis in a wide range of pathological conditions such as cardiovascular diseases, cancer, immune deficiency and hepatotoxicity. Although conclusive clinical data in humans is still missing, recent research results have suggested that some of the active ingredients ginseng exert beneficial effects on central nervous system (CNS) disorders and neurodegenerative diseases, suggesting it could be used in treatment of psychotic disorders. Data from neural cell cultures and animal studies contribute to the understanding of these mechanisms that involve inhibitory effects on stress-induced corticosterone level increasing and modulating of neurontransmitters, reducing $Ca^{2+}$ over-influx, scavenging of free radicals and counteracting excitotoxicity. In this review, we focused on recently reported medicinal effects of ginseng and summarized the possibility of its applications on psychotic disorders.

• Food Science of Animal Resources
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• v.40 no.5
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• pp.860-861
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• 2020

• Electronics and Telecommunications Trends
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• v.33 no.6
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• pp.58-68
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• 2018

A neuromorphic hardware that mimics biological perceptions and has a path toward human-level artificial intelligence (AI) was developed. In contrast with software-based AI using a conventional Von Neumann computer architecture, neuromorphic hardware-based AI has a power-efficient operation with simultaneous memorization and calculation, which is the operation method of the human brain. For an ideal neuromorphic device similar to the human brain, many technical huddles should be overcome; for example, new materials and structures for the synapses and neurons, an ultra-high density integration process, and neuromorphic modeling should be developed, and a better biological understanding of learning, memory, and cognition of the brain should be achieved. In this paper, studies attempting to overcome the limitations of next-generation neuromorphic hardware technologies are reviewed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3390-3405
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• 2022

Long Short-Term Memory (LSTM) combined with attention mechanism is extensively used to generate semantic sentences of images in image captioning models. However, features of salient regions and spatial information are not utilized sufficiently in most related works. Meanwhile, the LSTM also suffers from the problem of underutilized information in a single time step. In the paper, two innovative approaches are proposed to solve these problems. First, the Synergy-Gated Attention (SGA) method is proposed, which can process the spatial features and the salient region features of given images simultaneously. SGA establishes a gated mechanism through the global features to guide the interaction of information between these two features. Then, the Recurrent Fusion LSTM (RF-LSTM) mechanism is proposed, which can predict the next hidden vectors in one time step and improve linguistic coherence by fusing future information. Experimental results on the benchmark dataset of MSCOCO show that compared with the state-of-the-art methods, the proposed method can improve the performance of image captioning model, and achieve competitive performance on multiple evaluation indicators.

• 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.