• Smart Media Journal
• /
• v.11 no.4
• /
• pp.19-29
• /
• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Composites Research
• /
• v.32 no.5
• /
• pp.216-221
• /
• 2019

Piecewise integrated composite (PIC) beam has different stacking sequences for several regions with respect to their superior load-resisting capabilities. On the interest of current research is to improve bending characteristics of PIC beam, with assigning specific stacking sequence to a specific region with the help of machine learning techniques. 240 elements of from the FE model were chosen to be reference points. Preliminary FE analysis revealed triaxialities at those regularly distributed reference points to obtain learning data creation of machine learning. Triaxiality values catagorise the type of loading i.e. tension, compression or shear. Machine learning model was formulated by learning data as well as hyperparameters and proper load fidelity was suggested by tuned values of hyperparameters, however, comparatively higher nonlinearity intensive region, such as side face of the beam showed poor load fidelity. Therefore, irregular distribution of reference points, i.e., dense reference points were distributed in the severe changes of loading, on the contrary, coarse distribution for rare changes of loading, was prepared for machine learning model. FE model with irregularly distributed reference points showed better load fidelity compared to the results from the model with regular distribution of reference points.

• Journal of Bio-Environment Control
• /
• v.31 no.3
• /
• pp.152-162
• /
• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.419-422
• /
• 2019

The recent increase in single-person households and family income has led to an increase in the number of pet owners. However, due to the owners' difficulty to communicate with them for 24 hours, pets, and especially dogs, tend to display unwanted behavior that can be harmful to themselves and their environment when left alone. Therefore, detecting those behaviors when the owner is absent is necessary to suppress them and prevent any damage. In this paper, we propose a camera-based system that detects a set of normal and unwanted behaviors using deep learning algorithms to monitor dogs when left alone at home. The frames collected from the camera are arranged into sequences of RGB frames and their corresponding optical flow sequences, and then features are extracted from each data flow using pre-trained VGG-16 models. The extracted features from each sequence are concatenated and input to a bi-directional LSTM network that classifies the dog action into one of the targeted classes. The experimental results show that our method achieves a good performance exceeding 0.9 in precision, recall and f-1 score.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.220-228
• /
• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• The Research Journal of the Costume Culture
• /
• v.28 no.2
• /
• pp.166-180
• /
• 2020

This oral history study explores the past and present Japanese menswear custom-tailoring culture. Two master tailors with more than 50 years of experience and two young tailors with less than a year of experience were interviewed about the tailor training process and environment, working conditions, thinking and behavior, and societal and governmental efforts for vitalizing custom-tailoring. The results indicate, first, a 10-year apprenticeship was required in the past, whereas young tailors today must register for three-year professional tailoring classes. Tailors then and now have been trained in a similar sequence of pants, vests, and jackets. Second, regarding working conditions, tailors had to provide gratitude services to their masters for a few years, even after their training ended. In contrast, young tailors today must continue a probationary period after their three-year schooling; however, they experience difficulty with finding tailoring shops for their probation. Third, in terms of thinking and behavior, master tailors learned their trade to earn a living, whereas young tailors today entered the field due to their interests in it. In addition, young tailors want a systematic learning process, whereas master tailors learned their skills while on the job. Last, tailoring academies today are run by tailoring shops and societies. Career exploration programs are offered to elementary and middle school students by the Kobe government in association with the local tailoring society to provide tailoring experiences.

• Journal of KIISE
• /
• v.44 no.1
• /
• pp.36-43
• /
• 2017

Syntactic information represents the dependency relation between predicates and arguments, and it is helpful for improving the performance of Semantic Role Labeling systems. However, syntax analysis can cause computational overhead and inherit incorrect syntactic information. To solve this problem, we exclude syntactic information and use only morpheme information to construct Semantic Role Labeling systems. In this study, we propose an end-to-end SRL system that only uses morpheme information with Stacked Bidirectional LSTM-CRFs model by extending the LSTM RNN that is suitable for sequence labeling problem. Our experimental results show that our proposed model has better performance, as compare to other models.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.1981-1995
• /
• 2021

When a person tries to conceal emotions, real emotions will manifest themselves in the form of micro-expressions. Research on facial micro-expression recognition is still extremely challenging in the field of pattern recognition. This is because it is difficult to implement the best feature extraction method to cope with micro-expressions with small changes and short duration. Most methods are based on hand-crafted features to extract subtle facial movements. In this study, we introduce a method that incorporates optical flow and deep learning. First, we take out the onset frame and the apex frame from each video sequence. Then, the motion features between these two frames are extracted using the optical flow method. Finally, the features are inputted into an improved MobileNetV2 model, where SVM is applied to classify expressions. In order to evaluate the effectiveness of the method, we conduct experiments on the public spontaneous micro-expression database CASME II. Under the condition of applying the leave-one-subject-out cross-validation method, the recognition accuracy rate reaches 53.01%, and the F-score reaches 0.5231. The results show that the proposed method can significantly improve the micro-expression recognition performance.

• Journal of The Korean Association of Information Education
• /
• v.18 no.3
• /
• pp.443-452
• /
• 2014

In this study, robot curriculum was developed to fulfill systematic robot education in elementary and middle schools. For literature research, robot curriculum cases in the UK, the US, and Korea were surveyed to choose and organize robot education contents. The robot curriculum developed is categorized into five areas-robot basic course, sensor, structure/motion, control, and problem-solving-and consist of four step-robot experience, robot creation, robot exploration, and robot control. Stepwise learning contents reflected difficulty and sequence to let students experience intensive repetitive activity. This study will lay the foundation for applying systematic robot education to elementary and middle school students. In the future, it is expected to study the development and application of various programs on the basis of the developed robot curriculum.

• Journal of Korea Multimedia Society
• /
• v.3 no.3
• /
• pp.241-252
• /
• 2000

In this paper, a translation, rotation and scale invariant system for the recognition of closed 2-D images using the bispectrum of a contour sequence and a weighted fuzzy classifier is derived and compared with the recognition process using one of the competitive neural algorithm, called a LVQ( Loaming Vector Quantization). The bispectrum based on third order cumulants is applied to the contour sequences of an image to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to the represent two-dimensional planar images and are fed into a weighted fuzzy classifier. The experimental processes with eight different shapes of aircraft images are presented to illustrate a relatively high performance of the proposed recognition system.