• ETRI Journal
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• v.40 no.5
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• pp.643-652
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• 2018

Dysarthria is a degenerative disorder of the central nervous system that affects the control of articulation and pitch; therefore, it affects the uniqueness of sound produced by the speaker. Hence, dysarthric speaker recognition is a challenging task. In this paper, a feature-extraction method based on deep belief networks is presented for the task of identifying a speaker suffering from dysarthria. The effectiveness of the proposed method is demonstrated and compared with well-known Mel-frequency cepstral coefficient features. For classification purposes, the use of a multi-layer perceptron neural network is proposed with two structures. Our evaluations using the universal access speech database produced promising results and outperformed other baseline methods. In addition, speaker identification under both text-dependent and text-independent conditions are explored. The highest accuracy achieved using the proposed system is 97.3%.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• Journal of Information Technology Services
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• v.9 no.3
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• pp.179-191
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• 2010

Service identification and composition is one of the key characteristics for a successful Service-Oriented Computing, being receiving a lot of attention from researchers in recent years. In the Service-Oriented Analysis, the identification of business services has to be preceded before application services are identified. Most approaches addressing the derivation of business services are based on heuristic methods and human experts. The manual identification of business services is highly expensive and ambiguous task, and it may result in the service design with bad quality because of errors and misconception. Although a few of approaches of automatic service identification are proposed, most of them are in focus on technical architectures and application services. In this paper, we propose a model on the automatic identification of business services by horizontal and vertical service alignment using Enterprise Architecture as an ontology. We verify the effectiveness of the proposed model of business services identification through a case study based on Department of Defense Enterprise Architecture.

• Phonetics and Speech Sciences
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• v.8 no.1
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• pp.37-44
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• 2016

The goal of the current study is to investigate how the sound change is reflected in production or in perception, and what the effect of lexical frequency is on the loss of sound contrasts. Specifically, the current study examined whether the vowel length contrasts are retained in Korean speakers' productions, and whether Korean listeners can distinguish vowel length minimal pairs in their perception. Two production experiments and two perception experiments investigated this. For production tests, twelve Korean native speakers in their 20s and 40s completed a read-aloud task as well as a map-task. The results showed that, regardless of their age group, all Korean speakers produced vowel length contrasts with a small but significant differences in the read-aloud test. Interestingly, the difference between long and short vowels has disappeared in the map task, indicating that the speech mode affects producing vowel length contrasts. For perception tests, thirty-three Korean listeners completed a discrimination and a forced-choice identification test. The results showed that Korean listeners still have a perceptual sensitivity to distinguish lexical meaning of the vowel length minimal pair. We also found that the identification accuracy was affected by the word frequency, showing a higher identification accuracy in high- and mid- frequency words than low frequency words. Taken together, the current study demonstrated that the speech mode (read-aloud vs. spontaneous) affects the production of the sound undergoing a language change; and word frequency affects the sound change in speech perception.

• Journal of The Korean Astronomical Society
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• v.34 no.1
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• pp.1-8
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• 2001

We present a fast reduction method of survey data obtained using a single-dish radio telescope. Along with a brief review of classical method, a new method of identification and elimination of negative and positive bad channels are introduced using cloud identification code and several IRAF (Image Reduction and Analysis Facility) tasks relating statistics. Removing of several ripple patterns using Fourier Transform is also discussed. It is found that BACKGROUND task within IRAF is very efficient for fitting and subtraction of base-line with varying functions. Cloud identification method along with the possibility of its application for analysis of cloud structure is described, and future data reduction method is discussed.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.758-763
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• 1994

The work discusses the problems of modelling of the process of acoustic signal generation in machines. We have pointed out that in the task of minimizing of both moise and vibration, the key problem is identification of sources and paths of propagation, both in terms of their location and of definition of their characteristic features. Properly conducted identification makes possible the use of relatively simple mathematical models and this fact is particularly important for a broad application of the proposed methods in practice.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• IE interfaces
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• v.9 no.3
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• pp.269-282
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• 1996

This paper introduced a procedure for improving the quality of group decision making in various task types. Emphasis is placed on the construction of the optimal GDSS(Group Decision Support System) design with identifying the group decision characteristics of decision stages on the various task types. Especially, the identification of outliers and the establishment of confidence limits in group decision making were stressed. Participants in group decision making whose 'opinions fall outside the group's tolerance level are further studied to annex the source of this variation. The result showed that a preparation stage in the generating idea-type task and a illumination stage in the negotiating-type task were critical. Furthermore, the degree of the disagreement was severe in the verification and the preparation stages on group decision making. This paper developed a general procedure for improving the quality of group decision making. The procedure presented helps in identifying those stakeholders whose opinions may significantly deviate from that of the group.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.59-68
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• 2013

Objective: The purpose of this paper is to introduce a task complexity model combining task design aspects and complexity dimensions and to explain an approach to identifying and organizing task complexity factors based on the model. Background: Task complexity is a critical concept in describing and predicting human performance in complex systems such as nuclear power plants(NPPs). In order to understand the nature of task complexity, task complexity factors need to be identified and organized in a systematic manner. Although several methods have been suggested for identifying and organizing task complexity factors, it is rare to find an analytical approach based on a theoretically sound model. Method: This study regarded a task as a system to be designed. Three levels of design ion, which are functional, behavioral, and structural level of a task, characterize the design aspects of a task. The behavioral aspect is further classified into five cognitive processing activity types(information collection, information analysis, decision and action selection, action implementation, and action feedback). The complexity dimensions describe a task complexity from different perspectives that are size, variety, and order/organization. Combining the design aspects and complexity dimensions of a task, we developed a model from which meaningful task complexity factors can be identified and organized in an analytic way. Results: A model consisting of two facets, each of which is respectively concerned with design aspects and complexity dimensions, were proposed. Additionally, twenty-one task complexity factors were identified and organized based on the model. Conclusion: The model and approach introduced in this paper can be effectively used for examining human performance and human-system interface design issues in NPPs. Application: The model and approach introduced in this paper could be used for several human factors problems, including task allocation and design of information aiding, in NPPs and extended to other types of complex systems such as air traffic control systems as well.