• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.409-410
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• 2022

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.119-124
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• 2022

The international gnss service (IGS) provides real-time service (RTS) orbit and clock correction applicable to the broadcast ephemeris of GNSS satellites. However, since the RTS correction cannot be received if the Internet connection is lost, the RTS correction should be predicted and used when a signal interruption occurs in order to perform stable precise point positioning (PPP). In this paper, PPP was performed by predicting orbit and clock correction using a long short-term memory (LSTM) algorithm in real-time during the signal loss. The prediction performance was analyzed by implementing the LSTM algorithm in RPI (raspberry pi), the processing speed of which is not high. Compared to the polynomial prediction model, LSTM showed excellent performance in long-term prediction.

• ETRI Journal
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• v.42 no.5
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• pp.761-772
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• 2020

This paper presents an automatic proficiency assessment method for a non-native Korean read utterance using bidirectional long short-term memory (BLSTM)-based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without prompted text. The proposed method with the prompted text performs (a) a speech feature extraction step, (b) a forced-alignment step using a native AM and non-native AM, and (c) a linear regression-based proficiency scoring step for the five proficiency scores. Meanwhile, the proposed method without the prompted text additionally performs Korean speech recognition and a subword un-segmentation for the missing text. The experimental results indicate that the proposed method with prompted text improves the performance for all scores when compared to a method employing conventional AMs. In addition, the proposed method without the prompted text has a fluency score performance comparable to that of the method with prompted text.

• JSTS:Journal of Semiconductor Technology and Science
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• v.8 no.1
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• pp.98-103
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• 2008

1T1C DRAM has been facing technological and physical constraints that make more difficult their further scaling. Thus there are much industrial interests for alternative technologies that exploit new devices and concepts to go beyond the 1T1C DRAM technology, to allow better scaling, and to enlarge the memory performance. The technologies of DRAM cell are changing from 1T1C cell type to capacitor-less 1T-gain cell type for more scalable cell size. But floating body cell (FBC) of 1T-gain DRAM has weak retention properties than 1T1C DRAM. FET-type 1T-FeRAM is not adequate for long term nonvolatile applications, but could be a good alternative for the short term retention applications of DRAM. The proposed nonvolatile refresh scheme is based on utilizing the short nonvolatile retention properties of 1T-FeRAM in both after power-off and power-on operation condition.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.627-637
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• 2020

In this paper, the 1.24 million elderly patient medical data (HIRA-APS-2014-0053) provided by the Health Insurance Review and Assessment Service and weather data are analyzed with generalized estimating equation (GEE) model and long short term memory (LSTM) based recurrent neural network (RNN) model to predict the number of disease occurrence. To this end, we estimate the patient's residence as the area of the served medical institution, and the local weather data and medical data were merged. The status of disease occurrence is divided into three categories(occurrence of disease of interest, occurrence of other disease, no occurrence) during a week. The probabilities of categories are estimated by the GEE model and the RNN model. The number of cases of categories are predicted by adding the probabilities of categories. The comparison result shows that predictions of RNN model are more accurate than that of GEE model.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.159-166
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• 2023

The COI gene is a sequence of approximately 650 bp at the 5' terminal of the mitochondrial Cytochrome c Oxidase subunit I (COI) gene. As an effective DeoxyriboNucleic Acid (DNA) barcode, it is widely used for the taxonomic identification and evolutionary analysis of species. We created a CNN-LSTM hybrid model by combining the gene features partially extracted by the Long Short-Term Memory ( LSTM ) network with the feature maps obtained by the CNN. Compared to K-Means Clustering, Support Vector Machines (SVM), and a single CNN classification model, after training 278 samples in a training set that included 15 genera from two orders, the CNN-LSTM hybrid model achieved 94% accuracy in the test set, which contained 118 samples. We augmented the training set samples and four genera into four orders, and the classification accuracy of the test set reached 100%. This study also proposes calculating the cosine similarity between the training and test sets to initially assess the reliability of the predicted results and discover new species.

• The Korea Journal of Herbology
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• v.34 no.3
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• pp.1-7
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• 2019

Objectives : Amyloid ${\beta}(A{\beta})$ could induce cognitive deficits through oxidative stress, inflammation, and neuron death in Alzheimer's disease (AD). This study was investigated the effect of Angelica keiskei KOIDZUMI (AK) on memory in $A{\beta}$-induced an AD model. Methods : AK was extracted uses 70% ethanol solvent. Total polyphenol and flavonoids content were obtained by the Folin-Ciocalteu and the Ethylene glycol colorimetric methods, respectively. The antioxidant activities were assessed through free radical scavenging assays using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis (3-ethylbenzothiazolin-6-sulfonic acid) (ABTS) methods. Intracerebroventrical (i.c.v) injection of $A{\beta}$ 1-42 was used to induce AD in male ICR mice, followed by administrations of 5, 10 or 20 mg/kg AK on a daily. Animals were subjected to short and long term memory behavior in Y-maze and passive avoidance test. Results : The total polyphenol and flavonoids contents of the AK extract were $88.73{\pm}6.36mg$ gallic acid equivalent/g, $84.21{\pm}5.04mg$ rutin equivalent/g, respectively. The assays of DPPH and ABTS revealed that AK extract in treated concentrations (31.25, 62.5, 125, 250, 500, $1000{\mu}g/m{\ell}$) increased antioxidant activity in a dose-dependent manner. Oral administration of AK extract significantly reversed the $A{\beta}$ 1-42-induced decreasing of the spontaneous alternation in the Y-maze test and $A{\beta}$ 1-42-induced shorting of the step-through latency in the passive avoidance test. Conclusions : The findings suggest that AK indicated the antioxidant protective effects against $A{\beta}$-induced memory deficits, and therefore a potential lead natural therapeutic drug or agent for AD.