• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.1
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• pp.51-70
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• 2001

Characteristics of handwriting were investigated and compared between the patients with writing disabilities and normal elementary school pupils. Generally, the heights of the letters of the patients were significantly larger than those of normal children, and letters of the patients were more sparsely distributed than those of controls. The distance between the words were significantly reduced in the patients’ writings, which indicated that patients had much more problems of space-leaving than normal pupils. Letter heights differences were significant across all grades in the patients and normal controls. The heights of the letters decreased as they grew older, and the slope of the decrements were more steeper in normal girls(r=-0.45) than girls with writing disabilities(r=-0.16). Sex differences were found in the letter spacings in low grades(grades 1, 2), that is, the distances between the letters were significantly narrower in the male patients than normal boys in these grades, and the differences were almost indiscriminating in grades 3 through 5, and finally, in sixth grade, letter spacings were signifycantly broader in normal boys than male dysgraphics. In girls, letter spacings were significantly broader in the patients across all grades. These findings supports the hypothesis that male and female writings were qualitatively different and that distinct mechanisms served in boys and girls dysgraphics. Across all grades and sexes, spaces between the words of the patients were significantly broader than normal pupils, which suggested that space-leaving between the words was important in Korean writings. There was trend that letter spacings and word spacings decreased across grades, but in girls, no correlations between the letter spacings and grades were found. Correlation analyses revealed that letter heights and letter spacings had mild correlation(r=0.11-0.15), and that letter spacings and word spacings had robust correlation(r=0.99). Phonological errors were mostly found in last phoneme(Jong-seong), especially double-phoneme(ㄳ, ㄵ, ㄶ, ㄺ, ㄻ, ㄼ, ㄾ, ㄿ, ㅀ, ㅄ), and in the case the sound values changed due to assimilations of phonemes. Semantic errors were rare in both groups. Space-leaving errors were correlated with phonological errors, and more frequent in boys than girls. In conclusion, significant differences existed in the letter heights, letter spacings, word spacings, and frequencies of phonological errors and spaceleaving errors between the patients with writing disabilities and normal pupils. The characteristics of writings changed across grades and the developmental profiles were somewhat quantitatively different between the groups. The differences became obvious from the second-third grades.