• Journal of Electrochemical Science and Technology
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• v.15 no.2
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• pp.299-313
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• 2024

Electrochemical machining (ECM) is an effective manufacturing method for difficult-to-machine materials and is widely used in the precision manufacturing of aerospace components. In recent years, the requirements for the machining accuracy and surface integrity of ECM have become increasingly stringent. To further improve the machining quality, this work investigated the intricate laws between electrolyte filtration accuracy and machining quality. Electrolytes with different filtration accuracies were compared, and a numerical simulation was used to evaluate the change in temperature and bubble rate of the flow field in the machining area. Experiments were conducted on ECM of Ti-6Al-4V (TC4) alloy workpieces using electrolytes with different filtration accuracy. The workpiece machining accuracy and surface quality were analyzed, and the repetition accuracy of the workpiece was evaluated. The intricate laws between electrolyte filtration accuracy and machining quality were explored. It was found that when the electrolyte filtration accuracy is improved, so too is the machining quality of the ECM. However, once the filtration accuracy has reached a certain value, the machining quality has extremely limited improvement. By evaluating the repetition accuracy of processed workpieces in electrolytes with different filtration accuracies, it was found that when the filtration accuracy reaches a certain value, there is no positive correlation between the repetition accuracy and filtration accuracy. The result shows that, for the workpiece material and conditions considered in this paper, an electrolyte with 0.5㎛ filtration accuracy is suitable for the wide application of precision ECM.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.147-154
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• 2019

In this paper, we conducted a study that utilizes deep learning to calculate appropriate physical exercise information when basic human factors such as sex, age, height, and weight of users come in. To apply deep learning, a method was applied to calculate the amount of fat needed to calculate the amount of one repetition maximum by utilizing the structure of the basic Deep Neural Network. By applying Accuracy improvement methods such as Relu, Weight initialization, and Dropout to existing deep learning structures, we have improved Accuracy to derive a lean body weight that is closer to actual results. In addition, the results were derived by applying a formula for calculating the one repetition maximum load on upper and lower body movements for use in actual physical exercise. If studies continue, such as the way they are applied in this paper, they will be able to suggest effective physical exercise options for different conditions as well as conditions for users.

• MALSORI
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• no.58
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• pp.1-18
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• 2006

The purpose of this study was to investigate diadochokinetic (DDK) characteristics in the subjects with spastic cerebral palsy (CP) by severity. DDK characteristics were measured through rate, regularity, accuracy and consistency in Alternate Motion rate (AMR) and Sequential Motion rate (SMR) tasks. The subjects participated in this study included 27 subjects with spastic CP (mild- 9, moderate- 9, severe- 9) and 9 normal persons who are around 11-20 years old. On the result of this study, rate in AMR was significant difference between all spastic groups and normal group, and rate in SMR was significant difference between normal and mild groups and moderate and severe groups. In regularity of the DDK tasks, severe group had significant difference the other groups. Finally, accuracy and consistency of the DDK tasks exhibited significant difference between all spastic groups and normal group. In conclusion, the subjects with spastic CP have a tendency to produce slow and irregular syllable repetition as severity increases, but to produce inaccurate and inconsistent syllable repetition regardless of severity in the DDK tasks.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.190-195
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• 2021

Currently, one of the fields leading the 4th industrial revolution is the image recognition field of artificial intelligence, which is showing good results in many fields. In this paper, using is a YOLO V2 model, which is one of the image recognition models, we intend to classify and select into three types according to the characteristics of fruits. To this end, it was designed to proceed the number of iterations of learning 9000 counts based on 640 mandarin image data of 3 classes. For model evaluation, normal, rotten, and unripe mandarin oranges were used based on images. We as a result of the experiment, the accuracy of the learning model was different depending on the number of learning. Normal mandarin oranges showed the highest at 60.5% in 9000 repetition learning, and unripe mandarin oranges also showed the highest at 61.8% in 9000 repetition learning. Lastly, rotten tangerines showed the highest accuracy at 86.0% in 7000 iterations. It will be very helpful if the results of this study are used for fruit farms in rural areas where labor is scarce.

• Speech Sciences
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• v.11 no.2
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• pp.105-117
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• 2004

Recently, many scholars have emphasized the importance of English listening ability for smoother communication. Most audio materials, however, were recorded in a quiet sound-proof booth. Therefore, students who have spent so much time listening to the ideal audio materials are expected to have difficulty communicating with native speakers in the real life. In this study, we examined how well thirty three Korean university students and five native speakers will repeat the recorded English sentences under noise and speed conditions. The subjects' production was scored by listening to each recorded sentence and counting the number of words correctly produced and determined the percent ratios of correctly produced words to the total words in each sentence. Results showed that the student group correctly repeated around 65% of all the words in each sentence while the native speakers demonstrated almost perfect match. It seemed that the students had difficulty perceiving and repeating function words in various conditions. Also, high-proficiency student group outperformed the low-proficiency student group particularly in their repetition of function words. In addition, the student subjects' accuracy of repetition remarkably dropped when the normal sentences were both sped up and mixed with noise. Finally, it was observed that the Korean students' percent correct ratio fell down as the stimulus sentence became longer.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2275-2280
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• 2009

u-Healthcare service is one of attractive applications in ubiquitous environment. In this paper, we propose a method to recognize exercises using a new accelerometer based body-attached platform for supporting u-Healthcare service. The platform consists of a device for measuring accelerometer data and a device for receiving the data. The former measures a user's motion data using a 3-axis accelerometer. The latter transmits the accelerometer data to a computer for recognizing the user's exercise. The algorithm for exercise recognition classifies the type of exercise using principle components analysis(PCA) from the accelerometer data transformed by discrete fourier transform(DFT), and estimates the repetition count of the recognized exercise using a peak detection algorithm. We evaluate the performance of the algorithm from the accuracy of the recognition of exercise type and the error rate of the estimation of repetition count. In our experimental result, the algorithm shows the accuracy about 98%.

• Laser Solutions
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• v.16 no.2
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• pp.1-6
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• 2013

Picosecond lasers are a very effective tool for micromachining metals, especially when high accuracy, high surface roughness and no heat affected zone are required. However, low productivity has been a limit to broadening the spectrum of their industrial applications. Recently it was reported that in the micromachining of copper with a 1064nm picosecond laser, there exist the optimal pulse energy and repetition rate to achieve the maximum volume ablation rate. In this paper, we used a 515nm picosecond laser, which is more efficient for micromachining copper in terms of laser energy absorption, to obtain its optimal pulse energy and repetition rate. Theoretical analysis based on the experimental data on copper ablation showed that using a 515nm picosecond laser instead of a 1064nm picosecond laser is more favorable in that the calculated threshold fluence is 75% lower and optical penetration depth is 50% deeper.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.99-103
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• 2012

Presently, A glass is widely used in telecommunication system, optoelectronic devices and micro electro mechanical systems. Micro drilling of glass using the laser can save processing cost and improve the accuracy. This paper experiments micro drilling using KrF excimer laser on the pyrex glass of $500{\mu}m$ thickness. We have experiment to find out optimum laser machining conditions of micro drilling of glass and ablation depth and influence by processing parameter suc'h pulse repetition rate, energy density and number of pulses. Pulse repetition rate don't influence ablation depth at the micro drilling of pyrex glass. Energy density influence micro drilling of parallelism and maximum thickness that can be drilled. Ablation depth is most influenced by number of pulses.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.12 no.1
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• pp.55-60
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• 2001

Background and Objectives : Syllable Maximum Repetition Rate(MRR) is ability to repeat rapidly the articulators and is assessed for oromechanism function as one of the MPT. MRR is measured by rate(counts/sec), also simultaneously considered accuracy and consistency. The objective of the present was to examine stimuli effects and age and sex differences for MRR. Materials and Method : This study was participated 60 normal males and females(1 : 1) who were divided into two groups young(<40 years old) and old($\geq$40 years old). Stimuli were $/{P^=}a/,/{t^=}a/,/{k^=}a/,/{P^h}a/,/{t^h}a/,/{k^h}a/,/{P^=}{at^=}{ak^=}a/$ for, manner(tense and aspirated) of articulation, $/{p^h}{at^h}{ak^h}a/,/{t^h}{ap^h}{ak^h}a/$ for the effect of the order of syllable, glide /u-i/ for coordination of lip and tongue, interrupted vowel /i/ for laryngeal function. Results : There were little differences in two age groups and sex and manner of articulation for MRR tasks. The fastest average MRR of the single syllable included in this study was $/{t^=}a/$. significant differences existed between MRR for ${p^h}{at^h}{ak^h}a/ and /{t^h}{ap^h}{ak^h}a/$, which suggested that MRR was affected by the order of the syllables. MRR for interrupted vowel /i/ was about 2 counts/sec slower than average rate of 1 syllables. Conclusion : From these results, the order of the syllables was a crucial variable in MRR. rather than age or sex. There were no difference age, sex and manner of rate. The interrupted vowel repetition rate was slightly slow used and can provide basic information to assess the speech mechanism and can be useful to develop effective stimuli to differentiate the disordered group from normal.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1543-1546
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• 2005

This paper presents a method to measure a large surface shape using atomic force microscopy, which has been used mostly for measuring over very tiny surfaces. Experiments are performed to measure a step height and a slope of a test sample. The proposed method is rigorously compared with the coordinate measuring machine. The repetition accuracy and the effects of the set point are also studied. The experimental results show that the proposed method is reliable and should be effective to measure both the nano-accuracy surface profile as well as the micro-accuracy global shape of a macro/micro parts using atomic force microscope.