• International Journal of Computer Science & Network Security
• /
• 제22권3호
• /
• pp.61-66
• /
• 2022

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

• 반도체디스플레이기술학회지
• /
• 제19권3호
• /
• pp.124-129
• /
• 2020

Autonomous underwater vehicle makes attracts to many researchers. This paper proposes a convolutional neural network (CNN) based fish detection method. Since there are not enough data sets in the process of training, overfitting problem can be occurred in deep learning. To solve the problem, we apply the dropout algorithm to simplify the model. Experimental result showed that the implemented method is promising, and the effectiveness of identification by dropout approach is highly enhanced.

• Journal of Information Processing Systems
• /
• 제14권6호
• /
• pp.1318-1330
• /
• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• International journal of advanced smart convergence
• /
• 제12권4호
• /
• pp.88-97
• /
• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2021년도 추계학술발표대회
• /
• pp.927-929
• /
• 2021

최근 펫팸족(Pet-Family)과 같이 반려동물을 가족처럼 생각하는 가구가 증가하면서 반려동물 시장이 크게 성장하고 있다. 이러한 이유로 본 논문에서는 반려동물의 객체 식별을 통한 객체 분할과 신체 좌표추정에 기반을 둔 반려동물의 행동 분류 방법을 제안한다. 이 방법은 CCTV를 통해 반려동물 영상 데이터를 수집한다. 수집된 영상 데이터는 반려동물의 인스턴스 분할을 위해 Mask R-CNN(Region Convolutional Neural Networks) 모델을 적용하고, DeepLabCut 모델을 통해 추정된 신체 좌푯값을 도출한다. 이 결과로 도출된 영상 데이터와 추정된 신체 좌표 값은 CNN(Convolutional Neural Networks)-LSTM(Long Short-Term Memory) 모델을 적용하여 행동을 분류한다. 본 모델을 바탕으로 행동을 분석 및 분류하여, 반려동물의 위험 상황과 돌발 행동에 대한 올바른 대처를 제공할 수 있는 기반을 제공할 것이라 기대한다.

• Current Optics and Photonics
• /
• 제8권1호
• /
• pp.80-85
• /
• 2024

Numerical values of the Auger coefficient and the free carrier absorption (FCA) coefficient are extracted by applying deep neural networks (DNNs) to the L-I characteristics of 850 nm GaAs/AlGaAs laser diodes. Two elemental DNNs are used to extract each coefficient sequentially. The fidelity of the extracted values is established through meticulous correlation of L-I characteristics bridging the realms of simulations and measurements. The methodology presented in this paper offers a way to accurately extract the Auger and FCA coefficients, which were traditionally treated as fitting parameters. It is anticipated that this approach will be applicable to other types of opto-electronic devices as well.

• Nuclear Engineering and Technology
• /
• 제53권7호
• /
• pp.2334-2340
• /
• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Nuclear Engineering and Technology
• /
• 제53권2호
• /
• pp.657-665
• /
• 2021

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

• 방송공학회논문지
• /
• 제25권6호
• /
• pp.854-860
• /
• 2020

심층 신경망은 영상 분류 그리고 음성 인식 등 다양한 분야에서 뛰어난 성능을 보여주었다. 그 중에서 데이터 증대를 통해 생성된 다양한 데이터는 신경망의 성능을 향상하게 시키는 데 중요한 역할을 했다. 일반적으로 데이터의 변형을 통한 증대는 신경망이 다채로운 예시를 접하고 더 일반적으로 학습되는 것을 가능하게 했다. 기존의 영상 분야에서는 신경망 성능 향상을 위해 새로운 증대 방법을 제시할 뿐만 아니라 데이터와 신경망의 구조에 따라 변화할 수 있는 최적의 데이터 증대 방법의 탐색 방법을 제안해왔다. 본 논문은 이에 영감을 받아 음향 분야에서 최적의 데이터 증대 방법을 탐색하는 것을 목표로 한다. 잡음 추가, 음의 높낮이 변경 혹은 재생 속도를 조절하는 등의 증대 방법들을 다양하게 조합하는 실험을 통해 경험적으로 어떤 증대 방법이 가장 효과적인지 탐색했다. 결과적으로 자연 음향 데이터 세트 (ESC-50)에 최적화된 데이터 증대 방법을 적용함으로써 분류 정확도를 향상하게 시킬 수 있었다.

• 한국ITS학회 논문지
• /
• 제18권3호
• /
• pp.106-118
• /
• 2019

최근 들어 인공 지능을 이용한 영상 객체 인식에 대한 연구 및 개발이 활발하게 진행되고 있다. 그 연장선상에서 도로 유지 및 관리 분야에도 관련 연구의 활용도가 크게 향상될 것으로 기대된다. 그 중에서도 특히 도로 노면 파손 객체 인식 (Object Detection) 을 위한 인공 지능모델이 지속적으로 개발되고 있다. 이러한 객체 인식 알고리즘을 개발하려면 우선적으로 특징지도를 생성하는 Backbone Network가 반드시 필요한데, 본 논문에서는 이를 선정하는 방법을 제안하고자 한다. 이를 위해 6,000여 장의 도로 노면 파손 영상 데이터를 확보하고, 근래에 많이 사용되는 4종류의 심층 신경망을 활용하여 성능을 비교한다. 3가지의 성능 평가 방법을 적용하여 심층 신경망의 특징을 분석하고 최적의 심층 신경망을 결정한다. 또한 하이퍼 파라미터의 최적 조율을 통해 성능을 향상시키고, 최종적으로 도로 노면 파손 영상 분류를 위하여 85.9%의 정확도로 수행이 가능한 경량화된 Backbone Network용 심층 신경망을 제안한다.