• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• Journal of Industrial Technology
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• v.43 no.1
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• pp.1-6
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• 2023

TiO₂ is a versatile metal oxide material that is frequently used as a photo-catalyst for organic pollutant oxidation and a functional material for ultraviolet-ray protection. To improve its chemical/physical properties and widen the range of industrial application, it is demanded to control the crystalline feature and morphology precisely by applying advanced nano-synthesis methods. In this study, we prepared TiO₂ nanoparticles using the water-in-oil (W/O) microemulsion method and compared them with the particles synthesized by the conventional precipitation method. Also, we tried to find the optimum conditions for obtaining nano-sized, anatase-rich TiO₂ particles by the W/O microemulsion method. We analyzed the crystalline feature and particle size of the prepared samples using X-ray diffraction (XRD) and Transmission electron microscopy (TEM). In summary, we found the W/O microemulsion is more effective than precipitation in obtaining nano-sized TiO₂. The best result was derived when the microemulsion was formed using AOT surfactant, hydrolysis was performed under basic condition and the sample was calcined at 200℃.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• Journal of Pharmacopuncture
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• v.27 no.3
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• pp.223-233
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• 2024

Objectives: The phytochemicals in the aerial parts of Euphorbia paralias (also known as Sea Spurge) and their anti-inflammatory and antimicrobial activities were investigated. Methods: The methanolic extract was characterized using GC-MS and HPLC techniques. The anti-inflammatory feature was estimated through a Human Red Blood Cell (HRBC) membrane stabilization technique, while the antimicrobial feature was evaluated by the disc diffusion agar technique, minimum bactericidal concentration, and minimum inhibitory concentration (MIC) via micro-broth dilution method. Results: The GC/MS results demonstrated the existence of various phytochemicals, such as n-hexadecenoic acid, cis-11-eicosenoic acid, and methyl stearate, recognized for their anti-inflammatory and antibacterial features. The similarity of the phytochemical composition with other Euphorbia species emphasizes the genus-wide similarity. The anti-inflammatory activity exhibited a noteworthy inhibitory effect comparable to the reference drug indomethacin. The extract's antimicrobial potential was tested against a range of microorganisms, demonstrating significant action against Gram-positive bacteria and Candida albicans. The quantification of total phenolics and flavonoids further supported the therapeutic potential of the extract. Conclusion: The methanolic extract from E. paralias emerges as a successful natural source of important active constituents with potential applications as anti-inflammatory and antimicrobial agents. This research provides a first step to valorize Euphorbia paralias insights as a source of worthwhile phytochemicals that have potential applications in the pharmaceutical industry.

• Journal of architectural history
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• v.18 no.3
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• pp.27-44
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• 2009

In Korea, the direction of houses are typically determined by considering the directional orientation and shape of the mountain range rather than ignoring the geographical feature of the mountain range. Traditional villages of Korea are known to have very particular ways of adopting the geomantic surroundings of natural environment. This is very true especially have a high mountain in the back and a lower mountain in front. At the same time, most of the houses tend to prefer south as a man direction so that they can receive more sun light. However, if the mountain range faces north, it will not be easy to determine the directional orientation of houses. This paper, therefore, tries to identify how the houses of villages facing north, direst the orientation. This, the northern village, solves the problem by facing all direction rather than one major direction. The houses of the villages facing north, tend to revise the direction by changing the back mountain(주산) or front mountain(인산) that helps them change the direction towards he range of eastern or western direction. As a result, the houses tend to the direction towards east and wes compared to north and south. The directional orientation of houses was clearly distributed or concentrated by depending of the shape and directional orientation of the mountain range. This kind of research let us know the relationship between the natural north direction, the direction of geomantic surrounding, and the direction of houses in traditional Korean villages.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.365-374
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• 2008

In this paper, we propose a model to enrich an ontology by incrementally extending the relations through variations of patterns. In order to generalize initial patterns, combinations of features are considered as candidate patterns. The candidate patterns are used to extract relations from Wikipedia, which are sorted out according to reliability based on corpus frequency. Selected patterns then are used to extract relations, while extracted relations are again used to extend the patterns of the relation. Through making variations of patterns in incremental enrichment process, the range of pattern selection is broaden and refined, which can increase coverage and accuracy of relations extracted. In the experiments with single-feature based pattern models, we observe that the features of lexical, headword, and hypernym provide reliable information, while POS and syntactic features provide general information that is useful for enrichment of relations. Based on observations on the feature types that are appropriate for each syntactic unit type, we propose a pattern model based on the composition of features as our ongoing work.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.395-403
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• 2016

Recently, Image processing has been used in many areas. In the image processing techniques that a lot of research is tracking of moving object in real time. There are a number of popular methods for tracking an object such as HOG(Histogram of Oriented Gradients) to track pedestrians, and Codebook to subtract background. However, object extraction has difficulty because that a moving object has dynamic background in the image, and occurs severe lighting changes. In this paper, we propose a method of object extraction using depth image and color image features based on ROI(Region of Interest). First of all, we look for the feature points using the color image after setting the ROI a range to find the location of object in depth image. And we are extracting an object by creating a new contour using the convex hull point of object and the feature points. Finally, we compare the proposed method with the existing methods to find out how accurate extracting the object is.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.3
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• pp.174-181
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• 2011

A new registration method for IR image and EO image is proposed in this paper. IR sensor is applicable to many area because it absorbs thermal radiation energy unlike EO sensor does. However, IR sensor has difficulty to extract and match features due to low contrast compared to EO image. In order to register both images, we used modified SIFT(Scale Invariant Feature Transform) and block processing to increase feature distinctiveness. To remove outlier, we applied RANSAC(RANdom SAample Concensus) for each block. Finally, we unified matching features into single coordinate system and remove outlier again. We used 3~5um range IR image, and our experiment result showed good robustness in registration with IR image.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.484-491
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• 2015

Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.