• Journal of Information Technology Applications and Management
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• 제29권5호
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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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.409-410
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• 2022

본 논문은 LSTM(Long-Short Term Memory) 네트워크와 CNN 딥러닝 기법을 기반으로 하는 융합 모델을 제안하고 다중 카테고리 뉴스 데이터 세트에 적용하여 좋은 결과를 얻었다. 실험에 따르면 딥 러닝 기반의 융합 모델이 텍스트 감정 분류의 정밀도와 정확도를 크게 향상시켰다. 이 방법은 모델을 최적화하고 모델의 성능을 향상시키는 중요한 방법이 될 것이다.

• International Journal of Computer Science & Network Security
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• 제23권3호
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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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• 제23권5호
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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%.

• 한국항행학회논문지
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• 제26권2호
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• pp.119-124
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• 2022

IGS (international gnss service)에서는 GNSS (global navigation satellite system) 위성의 항법메시지에 적용할 수 있는 RTS (real-time service) 궤도 및 시계 보정정보를 제공한다. 하지만, 인터넷 단절이 발생하면 RTS 값을 수신할 수 없으므로, 안정적인 PPP (precise point positioning)를 수행하기 위해 신호 단절이 발생한 경우 RTS 보정정보를 예측해서 사용해야 한다. 본 논문에서는 실시간으로 신호 단절 구간에서 LSTM (long short-term memory) 알고리듬으로 궤도 및 시계 보정정보를 예측하여 PPP를 진행하였다. 연산 처리 속도가 빠르지 않은 Raspberry Pi (RPI)에 LSTM 알고리듬을 구현하여 예측성능을 분석하였다. 다항식 예측기법과 비교하여 LSTM은 장기간 예측에서 우수한 성능을 보였다.

• ETRI Journal
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• 제42권5호
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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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• 제8권1호
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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.

• 응용통계연구
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• 제33권5호
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• pp.627-637
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• 2020

본 논문에서는 건강보험심사평가원에서 제공한 약 120만명의 2014년 고령환자의료자료(HIRA-APS-2014-0053)과 기상자료를 일반화추정방정식(generalized estimating equation; GEE) 모형과 long short term memory (LSTM) 기반 순환신경망(recurrent neural network; RNN) 모형으로 분석하여 기상 조건에 따른 주요 주상병의 발생 빈도를 예측한다. 이를 위해 환자가 의료 서비스를 받은 기관의 지역을 이용하여 환자의 거주지를 추정하고 해당 지역의 주별 기상 관측소 자료와 의료자료를 병합하였다. 질병 발생 상태를 세 개의 범주(질병에 걸리지 않음, 관심 주상병 발생, 다른 질병 방생)로 나누었으며 각 범주에 속할 확률을 GEE 모형과 RNN 모형으로 추정하였다. 각 범주별 발생 건수는 해당 범주의 속할 추정확률의 합으로 계산하였으며 비교분석결과 RNN을 이용한 예측이 GEE를 이용한 예측보다 정확도가 높은 것으로 나타났다.

• Journal of information and communication convergence engineering
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• 제21권2호
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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.

• 대한본초학회지
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• 제34권3호
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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.