• 대한음성학회지:말소리
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• 제59호
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• pp.53-68
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• 2006

We report the evaluation results of the Korean speech recognition platform called ECHOS. The platform has an object-oriented and reusable architecture so that researchers can easily evaluate their own algorithms. The platform has all intrinsic modules to build a large vocabulary speech recognizer: Noise reduction, end-point detection, feature extraction, hidden Markov model (HMM)-based acoustic modeling, cross-word modeling, n-gram language modeling, n-best search, word graph generation, and Korean-specific language processing. The platform supports both lexical search trees and finite-state networks. It performs word-dependent n-best search with bigram in the forward search stage, and rescores the lattice with trigram in the backward stage. In an 8000-word continuous speech recognition task, the platform with a lexical tree increases 40% of word errors but decreases 50% of recognition time compared to the HTK platform with flat lexicon. ECHOS reduces 40% of recognition errors through incorporation of cross-word modeling. With the number of Gaussian mixtures increasing to 16, it yields word accuracy comparable to the previous lexical tree-based platform, Julius.

• Journal of Information Processing Systems
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• 제19권2호
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• pp.139-148
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• 2023

Current food-traceability platforms suffer from problems such as inconsistent traceability standards, a lack of public credibility, and slow access to data. In this work, a combined code and identification method was designed that can achieve more secure product traceability using the dual anti-counterfeiting technology of a QR code and a hidden code. When the QR code is blurry, the hidden code can still be used to effectively identify food information. Based on this combined code, a food-safety traceability platform was developed. The platform follows unified encoding standards and provides standardized interfaces. Based on this innovation, the platform not only can serve individual food-traceability systems development, but also connect existing traceability systems. These will help to solve the problems such as non-standard traceability content, inconsistent processes, and incompatible system software. The experimental results show that the combined code has higher accuracy. The food-safety traceability platform based on the combined code improves the safety of the traceability process and the integrity of the traceability information. The innovation of this paper is invoking the combined code united the QR code's rapidity and the hidden code's reliability, developing a platform that uses a unified coding standard and provides a standardized interface to resolve the differences between multi-food-traceability systems. Among similar systems, it is the only one that has been connected to the national QR code identification platform. The project has made profits and has significant economic and social benefits.

• 대한음성학회지:말소리
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• 제51호
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• pp.151-165
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• 2004

For educational and research purposes, a Korean speech recognition platform is designed. It is based on an object-oriented architecture and can be easily modified so that researchers can readily evaluate the performance of a recognition algorithm of interest. This platform will save development time for many who are interested in speech recognition. The platform includes the following modules: Noise reduction, end-point detection, met-frequency cepstral coefficient (MFCC) and perceptually linear prediction (PLP)-based feature extraction, hidden Markov model (HMM)-based acoustic modeling, n-gram language modeling, n-best search, and Korean language processing. The decoder of the platform can handle both lexical search trees for large vocabulary speech recognition and finite-state networks for small-to-medium vocabulary speech recognition. It performs word-dependent n-best search algorithm with a bigram language model in the first forward search stage and then extracts a word lattice and restores each lattice path with a trigram language model in the second stage.

• Preventive Nutrition and Food Science
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• 제19권4호
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• pp.343-347
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• 2014

The present study investigated the effects of Vaccinium uliginosum L. (bilberry) on the learning and memory impairments induced by amyloid-${\beta}$ protein ($A{\beta}P$) 1-42. ICR Swiss mice were divided into 4 groups: the control ($A{\beta}40$-1A), control with 5% bilberry group ($A{\beta}40$-1B), amyloid ${\beta}$ protein 1-42 treated group ($A{\beta}1$-42A), and $A{\beta}1$-42 with 5% bilberry group ($A{\beta}1$-42B). The control was treated with amyloid ${\beta}$-protein 40-1 for placebo effect, and Alzheimer's disease (AD) group was treated with amyloid ${\beta}$-protein 1-42. Amyloid ${\beta}$-protein 1-42 was intracerebroventricular (ICV) micro injected into the hippocampus in 35% acetonitrile and 0.1% trifluoroacetic acid. Although bilberry added groups tended to decrease the finding time of hidden platform, no statistical significance was found. On the other hand, escape latencies of $A{\beta}P$ injected mice were extended compared to that of $A{\beta}40$-1. In the Probe test, bilberry added $A{\beta}1$-42B group showed a significant (P<0.05) increase of probe crossing frequency compared to $A{\beta}1$-42A. Administration of amyloid protein ($A{\beta}1$-42) decreased working memory compared to $A{\beta}40$-1 control group. In passive avoidance test, bilberry significantly (P<0.05) increased the time of staying in the lighted area compared to AD control. The results suggest that bilberry may help to improve memory and learning capability in chemically induced Alzheimer's disease in experimental animal models.

• 인터넷정보학회논문지
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• 제20권6호
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• pp.73-84
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• 2019

최근 서비스로서의 머신러닝(MLaaS) 개념은 데이터 자체를 제외하고 네트워크 서버, 스토리지 또는 데이터 과학자 없이도 생산적인 서비스 모델을 구축할 수 있다는 점에서 기계학습을 다루는 대부분의 산업 분야와 연구 그룹들의 많은 관심을 받고 있다. 그러나 과학 분야에서는 양질의 빅데이터를 확보하는 가정 자체가 커다란 도전이 된다. 즉, 연구자 간 연구 결과물의 공유가 쉽지 않을 뿐 아니라 과학기술 데이터의 비정형성 문제를 해결해야하는 문제가 선행된다. 본 논문에서 제안된 KISTI-ML 플랫폼은 과학기술 데이터를 위한 AI 모델 고속 개발 도구로서, 머신러닝에 익숙하지 않은 연구자들을 위해 웹 기반 GUI 인터페이스를 제공하고 연구자는 자신의 데이터를 이용하여 머신러닝 코드를 손쉽게 생성하고 구동할 수 있다. 또한 승인된 커뮤니티 멤버들을 중심으로 데이터셋 및 특징 추출에 사용되는 데이터전처리, 학습 네트워크 설계 등이 포함되는 프로그래밍 코드를 공유할 수 있는 환경을 제공한다.

• 스마트미디어저널
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• 제1권4호
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• pp.18-26
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• 2012

스마트폰 어플리케이션 수의 증가는 새로운 유저인터페이스에 대한 중요성을 증대시켰으며, 다양한 센서를 융합한 유저인터페이스 개발 연구의 관심을 유도하고 있다. 본 논문에서는 스마트폰에 있는 가속도 센서, 자계 센서, 자이로 센서 정보를 융합하여 사용자 모션 제스처를 인식할 수 있는 새로운 유저인터페이스를 제안한다. 제안 방법은 유합 센서 정보로부터 스마트폰의 3차원 방위 정보를 구하고, HMM(Hidden Markov Model)을 이용하여 손동작 제스처를 인식한다. 특히 제안된 스마트폰의 3차원 방위 좌표계를 구면 좌표계로 변환하는 양자화 방법은 기본 축 회전에 더욱 민감한 인식이 이루어지도록 하였다. 실험을 통하여 제안한 방법이 93%로의 인식률을 보였다.

• 한국컴퓨터정보학회논문지
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• 제25권5호
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• pp.27-36
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• 2020

본 논문은 지식기반의 서비스 융합을 추구하는 4차산업혁명의 핵심 기반인 데이터로부터 유용하지만 드러나지 않는 정보들을 추출하는 방식을 제안한다. IoT로 대표되는 초연결사회에서 빅데이터의 생성은 필연적이며 그로부터 최적의 서비스를 도출하기 위해서는 가치있는 데이터를 찾아내는 것은 최우선으로 수행되어야 한다. 다양한 디바이스로부터 엄청난 양의 데이터를 수집·저장·관리하고 통합하는 데이터중심 IoT 플랫폼은 일종의 미들웨어 솔루션으로, 플랫폼의 궁극적인 목적은 빅데이터를 적시적소에 맞게 가공 및 분석수행 후 가치 있는 결과를 도출하여 최적의 답안을 제시하는 것이다. 이는 데이터를 분석하는 효율적이고 정확한 알고리즘을 필요로 한다. 이를 위해 본 논문은 분산되어 생성되는 IoT 데이터로부터 유용 정보 추출을 위해 시맨틱을 고려하여 원데이터를 저장하는 특화된 구조체를 설계하고 제안한 구조체에 기반하여 가치있는 정보를 찾아내기 위한 알고리즘을 다양한 정의와 증명을 사용하여 제시한다.

• Journal of Nutrition and Health
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• 제39권3호
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• pp.219-224
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• 2006

This study was performed to determine the effect of soy isoflavones on brain development and function in rats. Forty Sprague-Dawley male rats were provided diets containing different levels of soy isoflavones for 6 weeks; 0 ppm (control), 50 ppm (low isoflavone intake; LI), 250 ppm (medium isoflavone intake; MI) and 500 ppm (high isoflavone intake; HI). Learning ability was evaluated by a Y-shaped water maze and the activity of acetylcholinesterase in brain was assayed after decapitation. Food intake and body weights as well as weights of brain, liver, spleen, heart and kidney showed no significant difference among the four groups, which means 500 ppm of isoflavones is safe. In the water maze test, the frequency of error counted when rats entered one end of the alley without platform was significantly lower in the HI group than in the control group, and the escape latency as swim time taken to escape on the hidden platform was significantly shorter in the HI group than in the LI and control groups. The activity of acetylcholinesterase of the brain was significantly higher in the HI and MI groups than in the control group. Therefore, the results indicate that isoflavones may improve the cognitive function without adverse effects.

• Journal of Ginseng Research
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• 제20권2호
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• pp.139-148
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• 1996

This study was designed to examine the effects of red ginseng total saponin and extract on spatial working memory in mice using Morris water maze. Two kinds of red ginseng saponin (No. 1 and No. 2) and three kinds of red ginseng extract (No. 1, No. 2 and No. 3) to have different PD/ PT ratio (No. 1=1.24, No.2=1.47 No.3=2.41) were prepared by mixing the different parts of red ginseng In different ratio. In acute administration of total saponin No. 1 or No. 2, escape time to reach to a hidden platform In a fixed location for training trials was significantly decreased as compared with control group and swimming time in the quadrant that had contained the platform was also significantly increased as compared with control group. In acute treatment of extract No. 1 or 1 No. 2, swimming time in the platformless quadrant was increased dose dependently as compared with control group, especially at dose of 200 mg/kg,bw swimming time was significantly Increased. Oral treatment of extract No. 1 (100 mg/kg, bw) for 7 days produced an increase of swimming time In the platformless quadrant but a decrease of swimming time in No.3-treated group (100 mg/kg, bw). These results show that red ginseng may improve spatial discrimination learning and spatial working memory of mice

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.