Showing 1–12 of 180 results

  • Development of a Japanese language learning support system for international students using video content

    Abstract: In recent years, globalization has progressed, and Japan’s international students have increased. However, many international students study while working part-time, and due to the impact of Covid-19, face-to-face conversation with people has become difficult. Therefore, regular study time alone has become insufficient for practicing the Japanese language. On the other hand, there are video distribution services that have become popular in recent years. Therefore, we thought we could create a language learning support system by using them. This research aims to develop a language learning support system that uses the subtitle function of a video distribution service to improve learning motivation and to solve the lack of time to learn a foreign language (the Japanese, in this case). This paper mainly reports on the development of the system by using those video content.

    Keywords: Language learning support system; Language Learning with Netflix; LLN extension; python VLC module.

  • EMG-Based Recognition Method of Finger Movement Impairment Level in Post-Stroke Patients Based on Fugl-Meyer Assessment

    Abstract:  The restoration of finger motor functions is considered difficult during rehabilitation due to the complexity of the underlying muscles. The Fugl-Meyer Assessment (FMA) method is used by doctors to manually assess the level of finger movement impairment. However, there is a risk of evaluation errors due to inherent subjectivity. Therefore, a new, more accurate method must be developed to predict the level of impairment. This study aimed to evaluate the impairment level of finger movement based on the FMA. EMG signals were recorded from four patients while performing seven movements, and feature extraction was performed. SVM and Random Forest were used to classify the level of impairment for each movement. The SVM model obtained good results in the fourth movement, with an accuracy of 91.7% and an F1 score of 0.78.

    Keywords: impairment level; finger movement; post-stroke patients; recognition.

  • Processing of Multi-valued Attributes Based on Sparse Matrix

    Abstract: Multi-value attribute has always been a difficult problem to deal with in machine learning. Most models are unique for data format matching. When it is multi-value, most models cannot be used directly. At the same time, a large number of multi-valued attributes will be encountered in the construction of medical model. These attributes often represent that patients have multiple symptoms. The processing methods of multi- valued attributes can be roughly divided into two categories, one is through data preprocessing, the other is through algorithm pattern matching. The solution to medical multi-valued attributes in this paper is mainly through preprocessing, from the perspective of multi-valued attribute representation and projection. The process is to use sparse matrix to represent multi- valued data, convert it to high-dimensional space, and then project it back to one dimension to complete the processing of such data.

    Keywords: Multi-value attributes; Sparse matrix; high- dimensional projection

  • Development of separation technology for dissimilar material bonding products of steel plate and CFRTP using the eddy current method

  • A Chinese text similarity algorithm based on Yake and neural network

    Abstract: Traditional text similarity algorithm has the disadvantage of  a large amount of text data and high complexity. Keywords are highly concentrated thematic ideas in the text. Extracting them can reduce the complexity of text similarity calculation. Therefore, this paper proposes a Chinese text similarity calculation method that integrates improved YAKE and neural network(YANN). With Aim to the  problem that Yet Another Keyword Extractor(YAKE) algorithm is not suitable for Chinese text keyword extraction, keyword candidate stage. First the new feature value of words is calculated by using word span, position, frequency, word context relevance, and the number of different sentences. Next we calculate the keyword score of each candidate word after synthesizing all the features values, and output the keywords in the ascending order of the score. Finally, the keyword set is inputted into the trained word2vec model for vectorization. Summation and averaging where the keyword vector values are derived from the trained word2vec model, and the similarity between different texts is calculated by cosine similarity. The experimental results show that the method proposed in this paper has better performance than other algorithms in Chinese text keyword extraction, and the similarity calculation results prove the merit of the method used.


    Keywords: Keyword Extraction; Word2vec; Text Similarity

  • Initial parameters of CNNs generated by Convolutional Sparse Representation with L1 error term

    Abstract: Convolutional Sparse Representation (CSR) approximates images with the convolutional sum of dictionary filters and corresponding sparse coefficients. To improve classification accuracy of Convolutional Neural Networks (CNNs), this paper proposes to use the dictionary filters generated by CSR as initial parameters of CNNs’ filters since the CSR filters express features of test images. Our method also estimates the error term of CSR with the L1 norm instead of the L2 norm to increase robustness against outliers in datasets for training. The results of experiments classifying CIFAR-10 show that the CNN using the initial parameters generated by the proposed method with the L1 error term shows the highest classification accuracy for small numbers of training images compared with the two methods: the proposed method with the L2 error term and the Xavier’s method.

    Keywords: Convolutional Sparse Representation, Convolutional Neural Network

  • Evaluation of the effects of cold and hot environmental temperatures on the distribution of whole facial skin temperature

    Abstract: During the Covid-19 pandemic, fever detection using infrared thermography became widespread. A person with a fever is detected based on the facial skin temperature measured in a non-invasive and free-of-restraint method. Recent studies have pointed out that the facial whole skin temperature, when measured immediately after entering a moderately moderate environment from a cold environment, is not practical for detecting persons with fever because it is greatly affected by the environmental temperature. On the other hand, the effect of cold and hot temperatures on the details of the entire face has not been evaluated. In this study, we compared the cold and hot environments and the acclimation to moderate temperatures to the effects of cold and hot environments on the whole face skin temperature distribution was evaluated in detail. The results showed that the periorbital area and side of the nose were least affected in the cold environment, and the side of the nose was least affected in the hot environment. And these parts are suggested to be suitable for core temperature estimation considering the environmental temperature.

    Keywords: facial skin temperature, environmental temperature, facial thermal image, core temperature

  • Development of flexible Text Input Device Based on Image Processing for Each Level of Disability Person

    Abstract: This research aims to develop a flexible input text device for physically disabled person using image processing. In proposed method we use a camera to detect the disabled person arm and hand movements via image processing technics, in order to identify these movements with exact disabled person intention on what he/she wants to convey. When the intention is detected, the disabled person can then input his/her intention or thought data through the device. The input method can be changed according to the user’s disability level and is expected to have a positive impact for the rehabilitation of the user.

    Keywords: Rehabilitation, Disability, Image processing, Input

  • Parallel Navigation of Multi-Drones using City Information for Search and Rescue Operations

    Abstract: The ongoing global warming causes an increased number of disasters around the globe and is going to have a severe impact on our society in the years to come. Thanks to the rapid development of technology, drones have emerged, and the Search and Rescue operation become effective and efficient. In this paper, we propose the assessment of multi-drones for Search and Rescue (SAR) operations. The proposed SAR uses an appropriate city model with a diverse population density where the multi-drones can be dispatched based on the city’s geographic information for the Search and rescue operation during a disaster.

    Keywords: Drone; Search and Rescue; Disaster; Operations

  • Borehole Imaging by Applying 3D Visual SLAM to Borehole Images Acquired in Forward Vision Camera System

    Abstract: In the drilling survey, there is a technique called borehole imaging, which examines the
    internal ground structure and the presence or absence of faults based on the unfolded panorama created by processing the borehole images taken by a camera. When we investigate the underground to prevent disasters such as landslides, it is desirable to use a portable and simple system including a monocular camera because we set up the system on the mountain slope. The problem with the monocular camera system is that the camera moves off the hole center and rotates around the hole axis. In order to generate the accurate unfolded images, it is necessary to obtain information about the accurate center and orientation of the camera moving inside the hole. For this purpose, we have developed a system to simultaneously estimate the camera pose and the hole center by applying SLAM technology to forward-looking images acquired in a forward vision camera system.

    Keywords: borehole imaging, panorama, visual SLAM, forward-looking images, monocular camera, optical flow, disaster prevention engineering, drilling survey.

  • EEG Signal Power Prediction Using DEAP Dataset

    Abstract: When we listen to music, our emotions can change due to changes in our brain response. Because of this, a large number of research projects for classifying the emotional response using brain signals when listening to music through machine learning techniques emerged. On the contrary, to our knowledge, there is no previous research attempting to estimate the dynamic changes in the electroencephalogram (EEG) response under music stimuli through machine learning techniques. Therefore, in this manuscript, we proposed an approach to predict and anticipate changes in the EEG signal under music stimuli. Using the DEAP dataset, we split the EEG response to music stimuli into one-second length frames. After that, we compared the changes in the power of the brain signal of consecutive frames through two one-tailed Wilcoxon rank-sum tests. This test allowed us to label the changes in the second frame as “lower”, “similar” or “higher” signal compared to the first frame. Then, we attempted to predict these changes using a Support-Vector Machine (SVM) classifier with stratified 5-fold validation with different input combinations (only music, only brain signal, or a combination of both). Due to the use of multi-label classification with imbalanced data, we measured the results through F1-Scores. Over chance level predictions of the changes of signal power were obtained when using the previous second brain signal for the different channels and bands, especially in the frontal F3 and F4 channels.

    Keywords: Electroencephalography (EEG), Music perception, Neural correlation, Machine learning, Signal classification.

  • Mixed-Precision Acceleration of Ultrasound Imaging Software