• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1015-1023
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• 1999

We propose nonlinear processing schemes for robust acquisition of direct-sequence spread spectrum (DS/SS) signals in wideband pulse jamming. To mitigate the interference effect due to impulse-like wideband jamming signals, the received signal is preprocessed by using the median filter, a simple order statistic filter Since only parts of the PN sequence are used for rapid acquisition, it is indispensable for analytic design of an acquisition scheme to have an appropriate model of the partial PN signal. The partial correlation of the median filtered PN signal is approximated by a two-piecewise linear model using an approximate upper bound. The acquisition performance of the proposed schemes is compared to that of other schemes. Finally, the analytic design is verified by computer simulation.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.56-61
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• 2016

본 논문에서는 Sequence-to-sequence 모델을 생성요약의 방법으로 한국어 문서요약에 적용하였으며, copy mechanism과 input feeding을 적용한 RNN search 모델을 사용하여 시스템의 성능을 높였다. 인터넷 신문기사를 수집하여 구축한 한국어 문서요약 데이터 셋(train set 30291 문서, development set 3786 문서, test set 3705문서)으로 실험한 결과, input feeding과 copy mechanism을 포함한 모델이 형태소 기준으로 ROUGE-1 35.92, ROUGE-2 15.37, ROUGE-L 29.45로 가장 높은 성능을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.2-4
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• 2023

Sequence data plays an important role in the field of intelligence, especially for industrial control, traffic control and other aspects. Finding abnormal parts in sequence data has long been an application field of AI technology. In this paper, we propose an anomaly detection method for sequence data using a diffusion model. The diffusion model has two major advantages: interpretability derived from rigorous mathematical derivation and unrestricted selection of backbone models. This method uses the diffusion model to predict and reconstruct the sequence data, and then detects the abnormal part by comparing with the real data. This paper successfully verifies the feasibility of the diffusion model in the field of anomaly detection. We use the combination of MLP and diffusion model to generate data and compare the generated data with real data to detect anomalous points.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• Journal of Korea Multimedia Society
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• v.1 no.1
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• pp.109-119
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• 1998

This paper describes a research result on automatic generation of Abstract Test Suite from INAP protocol in formal specifications by applying many existing related algorithms such as Rural Chinese Postman Tour and UIO sequence concepts. We use the I/O FSM generated from SDL specifications and a characterizing sequence concepts. We use the I/O FSM generated from SDL specifications and a characterizing sequence, called UIO sequence, is defined for the I/O FSM. The UIO sequence is combined with the concept of Rural Chinese Postman tour to obtain an optimal test sequence. It also proposes an estimation methodology of the fault courage for the Test Suite obtained by our method and their translation into the standardized test notation TTCN.

• Journal of the Korea Safety Management & Science
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• v.2 no.1
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• pp.77-87
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• 2000

BOM has been widely used to manufacturing, product design and scheduling. There are several bug differences between Manufacturing BOM and Engineering BOM, which cause a lot of problems. A study to integrate both manufacturing BOM and Engineering BOM is researching to solve those problems. Therefore, this research presents a extended Engineering BOM concepts considering assembly sequence model.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.9-20
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• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.139-146
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• 2015

This paper presents the solution to model matching of switched asynchronous sequential machines by corrective control. We propose a model of switched asynchronous sequential machines, in which the system can have different dynamics of asynchronous machines governed by a pre-determined sequence of switching. The control objective is to derive a corrective control law so that the stable state behavior of the closed-loop system can match that of a prescribed model. A new skeleton matrix is defined to represent the reachability of the switched asynchronous machine, and a novel control scheme is presented that interweaves the switching signal and the corrective control procedure. A design algorithm for the proposed controller is illustrated in a case study.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.325-332
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• 2017

In this paper, a survey of test sequence generation methods for testing the conformance of a protocol implementation to its specification is presented. The best known methods proposed in the literature are called transition tour, distinguishing sequence, characterizing sequence, and unique input/output sequence. Also, several variants of the above methods are introduced. Applications of these methods to the finite state machine model are discussed. Then, comparative analysis of the methods is made in terms of test sequence length. Finally, conclusions are given as follows. The T-method produces the shortest test sequence, but it has the worst fault coverage. The W-method tends to produce excessively long test sequences even though its fault coverage is complete. The problem with the DS-method is that a distinguishing sequence may not exist. The UIO-method is more widely applicable, but it does not provide the same fault coverage as the DS-method.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.