• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.25-30
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• 2024

Kodály hand signs are symbols that intuitively represent pitch and note names based on the shape and height of the hand. They are an excellent tool that can be easily expressed using the human body, making them highly engaging for children who are new to music. Traditional hand signs help beginners easily understand pitch and significantly aid in music learning and performance. However, Kodály hand signs have distinctive features, such as the ability to indicate key changes or chords using both hands and to clearly represent accidentals. These features enable the effective use of Kodály hand signs. In this paper, we aim to investigate the changes in recognition rates according to the complexity of scales by creating a device for learning Kodály hand signs, teaching simple Do-Re-Mi scales, and then gradually increasing the complexity of the scales and teaching complex scales and children's songs (such as "May Had A Little Lamb"). The learning device utilizes accelerometer and bending sensors. The accelerometer detects the tilt of the hand, while the bending sensor detects the degree of bending in the fingers. The utilized accelerometer is a 6-axis accelerometer that can also measure angular velocity, ensuring accurate data collection. The learning and performance evaluation of the Kodály learning device were conducted using Python.

• Korean Journal of Forensic Psychology
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• v.12 no.2
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• pp.171-197
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• 2021

The purpose of this study is to examine empirically how the lay people judge self-defense and what factors could affect it. A total of 651 participants aged 20 years and over were asked to answer, attitude toward interpersonal violence, and legal attitude questionnaire, all divided by the type of self-defense. Participants were assigned one of the three types of situations that were claimed to be self-defense, and were given articles and scenarios related to each type of self-defense before making self-defense judgments. In addition, the impact of personal factors on self-defense judgment was analyzed after the legal attitude, and the attitude toward interpersonal violence, which are personal factors, was also measured. The results showed that the rate of recognition of self-defense was the highest in the type of self-defense for oneself, but the rate of denial of self-defense against state agencies was much higher, indicating the opposite. Furthemore, negative articles on self-defense were found to affect the judgment of self-defense. In addition, it was found that the level of the attitude toward interpersonal violence and legal attitude of individual participants could affect the judgment of self-defense. The general public's judgment process and the factors that affect self-defense judgment may be considered to prevent biased judgment in actual jury trials. Finally, influence, and limitations of this study and suggestions of subsequent study were also discussed.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Journal of Korean society of Dental Hygiene
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• v.9 no.4
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• pp.753-767
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• 2009

Objectives : The study is to gain some basic material for the improvement of dental clinics' homepages through a survey investigation, in which college students of three Dental Hygienic departments participated in Kwangju and Cheollanamdo Province. Methods : In the investigation three factors were analyzed : the degree of knowledge on dental clinics' homepages, the degree of recognition on them, and whether they have paid a visit on them or not. A total of 509(96.8%) respondents are valid except 17sheets of responses. Results : 1. When asked about the degrees of knowledge on the formation of the homepages by students' years, the correct rate on Information Service was higher in a row of the second year, the first year, and the third year. And it shows statistically significant difference(p<0.05). In the part of Counseling Service, the rate of correct answers was highest in the second year, and then the first year and the third year. It also shows significant difference(p<0.05). In case of Visual Service, the second year got the highest rate of correct answers, and then the first year and the third year. Here is significant difference by the school years<0.01) 2. It was asked whether they have visited the dental clinics' homepages. The results are like this: 145 sophomores(28.5%) have visited them, and 115 juniors(22.6%) and 85 freshmen(16.7%), and it show significant difference (p<0.001). 3. It was asked how many sites they have visited. Among the freshmen, not a few students visited two sites (34, 9.9%), among sophomores 48 students visited five sites(13.9%), and among juniors the highest answers were two sites (41, 11.9%). It shows signigicant difference(p<0.01). 4. It was asked what is the purpose of the visits. At this 27 freshmen answered for having counseling(7.8%), and 80 sophomores(23.3%) and 43 juniors(12.5%) answered they visited them for the purpose of gaining some materials about their major. It shows significant difference(p<0.001). 5. It was asked with what opportunity they have visited them. They answered through searching activities like this : freshmen (68, 19.8%), sophomores (130, 37.9%), and juniors (98, 28.6%). It shows significant difference(p<0.05). 6. In regard with the recognition of the homepages, all the participants said that the management of the homepages are closely related with the images of the clinics($3.96{\pm}0.781$). But it is found that they do not think that the effective management of dental clinics' homepages is the task of dental hygienic workers as a part of dental hygienic($3.12{\pm}0.971$). 7. There is some difference concerned with the homepages among each group of students; sophomores have highest recognition on them and then juniors and freshmen, and it shows significant difference(p<0.01). In addition, those who have visited them show higher recognition than those who have never visited them(p<0.001). Conclusions : There are some differences among each group of students in regard with the formation service, the purpose of visiting them and such experiences, and the opportunities. Whereases they think that the management of the homepages are closely related with the images of the clinics, they do not think that the effective management of dental clinics' homepages is the task of dental hygienic workers as a part of dental hygienic. Therefore it is necessary to study actively for the qualitative improvement of the dental clinics' homepages, which will result in the higher recognition on the homepages by the dental hygienic students and the workers.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.517-524
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• 2015

The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Considering sparse signal feature, two novel compressed sensing schemes are proposed, which can reduce compressed sampling rate in contrast to the traditional scheme. One algorithm is dynamically adjusting compression ratio based on modulation recognition and identification of symbol rate, which can reduce compression ratio. Furthermore, without priori information of the modulation and symbol rate, another improved algorithm is proposed with the application potential in practice, which does not need to reconstruct the signals. The improved algorithm is divided into two stages, which are the approaching stage and the monitoring stage. The overall sampling rate can be dramatically reduced without the performance deterioration of the spectrum detection compared to the conventional static compressed sampling rate algorithm. Numerous results show that the proposed compressed sensing technique can reduce sampling rate by 35%, with an acceptable detection probability over 0.9.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.363-370
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• 2022

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.80-86
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• 2007

In this paper, we utilize generalized warped filterbanks to improve the performance of speaker recognition systems. At first, the performance of speaker identification systems is analyzed by varying the type of warped filterbanks. Based on the results that the error pattern of recognition system is different depending on the type of filterbank used, we combine the likelihood values of the statistical models that consist of the features extracting from multiple warped filterbanks. Simulation results with TIMIT and NTIMIT database verify that the proposed system shows relative improvement of identification rate by 31.47% and 15.14% comparing it to the conventional system.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.104-110
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• 2007

As the exploration of the seabed is extended ever further, automated recognition and classification of sonar images become increasingly important. However, most of the methods ignore the directional information and its effect on the image textures produced. To deal with this problem, we apply 2D Gabor filters to extract the features of sonar images. The filters are designed with constrained parameters to reduce the complexity and to improve the calculation efficiency. Meanwhile, at each orientation, the optimal Gabor filter parameters will be selected with the help of bandwidth parameters based on the Fisher criterion. This method can overcome some disadvantages of the traditional approaches of extracting texture features, and improve the recognition rate effectively.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.9
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.141-144
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• 2001

In this paper, a new algorithm for extracting and recognizing characters from video, without pre-knowledge such as font, color, size of character, is proposed. To improve the recognition rate for videos with complex background at low resolution, continuous frames with identical text region are automatically detected to compose an average frame. Using boundary pixels of a text region as seeds, we apply region filling to remove background from the character Then color clustering is applied to remove remaining backgrounds according to the verification of region filling process. Features such as white run and zero-one transition from the center, are extracted from unknown characters. These feature are compared with a pre-composed character feature set to recognize the characters.