• Journal of Information Processing Systems
• /
• v.16 no.4
• /
• pp.795-808
• /
• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• Journal of Information Processing Systems
• /
• v.15 no.3
• /
• pp.538-549
• /
• 2019

Cloud computing is the concept of providing information technology services on the Internet, such as software, hardware, networking, and storage. These services can be accessed anywhere at any time on a pay-per-use basis. However, storing data on servers is a challenging aspect of cloud computing. This paper utilizes cryptography and access control to ensure the confidentiality, integrity, and proper control of access to sensitive data. We propose a model that can protect data in cloud computing. Our model is designed by using an enhanced RSA encryption algorithm and a combination of role-based access control model with extensible access control markup language (XACML) to facilitate security and allow data access. This paper proposes a model that uses cryptography concepts to store data in cloud computing and allows data access through the access control model with minimum time and cost for encryption and decryption.

• Journal of Information Processing Systems
• /
• v.13 no.1
• /
• pp.57-67
• /
• 2017

In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

• Journal of Information Processing Systems
• /
• v.20 no.2
• /
• pp.263-272
• /
• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3791-3805
• /
• 2016

The visual attention mechanism includes 2 attention models, the bottom-up (B-U) and the top-down (T-D), the physiology of which have not yet been accurately described. In this paper, the visual attention mechanism is regarded as a Bayesian fusion process, and a visual attention model based on particle filter is proposed. Under certain particular assumed conditions, a calculation formula of Bayesian posterior probability is deduced. The visual attention fusion process based on the particle filter is realized through importance sampling, particle weight updating, and resampling, and visual attention is finally determined by the particle distribution state. The test results of multigroup images show that the calculation result of this model has better subjective and objective effects than that of other models.

• Journal of Information Processing Systems
• /
• v.16 no.6
• /
• pp.1250-1260
• /
• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• Journal of Information Processing Systems
• /
• v.15 no.6
• /
• pp.1378-1391
• /
• 2019

In recent years, automatic document summarization has been widely studied in the field of natural language processing thanks to the remarkable developments made using deep learning models. To decode a word, existing models for abstractive summarization usually represent the context of a document using the weighted hidden states of each input word when they decode it. Because the weights change at each decoding step, these weights reflect only the local context of a document. Therefore, it is difficult to generate a summary that reflects the overall context of a document. To solve this problem, we introduce the notion of a general context and propose a model for summarization based on it. The general context reflects overall context of the document that is independent of each decoding step. Experimental results using the CNN/Daily Mail dataset show that the proposed model outperforms existing models.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.1014-1017
• /
• 2020

Many natural language processing (NLP) models utilize pre-trained word embeddings to leverage latent information. One of the most successful word embedding model is the Skip-gram (SG). In this paper, we propose a Skipgram drop (SG-Drop) model, which is a variation of the SG model. The SG-Drop model is designed to reduce training time efficiently. Furthermore, the SG-Drop allows controlling training time with its hyperparameter. It could train word embedding faster than reducing training epochs while better preserving the quality.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.868-871
• /
• 2008

Model Checking 기법은 시스템을 검증하고 반례를 제시해 주는 검증 방법으로 최근에 여러 분야에서 쓰이고 있다. 하지만 임베디드 시스템과 같이 그 검증에 있어서 시간, 자원적인 제한을 가지고 있는 분야에서는 검증할 항목을 임의로 선택해서 하는 경우가 대부분이다. 따라서 이 논문에서는 검증에 있어서 우선시 해야 할 기능들을 효율적으로 선정하는 빈도 기반 모델 검증 기법을 제안하고자 한다.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.12 no.1
• /
• pp.81-95
• /
• 2009

The CDB(Constraint Database) model is a new paradigm for massive spatial data processing such as GIS(Geographic Information System). This paper will identify the limitation of the schema structure and query processing through prior spatial database research and suggest more efficient processing mechanism of constraint data model. We presented constraint model concept, presentation method, and the examples of query processing. Especially, we represented TIN (Triangulated Irregular Network) as a constraint data model which displays the height on a plane data and compared it with prior spatial data model. Finally, we identified that we were able to formalize spatial data in a simple and refined way through constraint data modeling.