• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.717-726
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• 2020

The physical characteristics of the major varieties of perilla were analyzed to use as basic data for the design of a high-quality, high-efficiency perilla cleaner and stone separator. Because the size, thousand-grain weight, angle of repose, angle of friction, bulk density and terminal velocity of perilla have significant differences according to the perilla variety, the different of characteristics by variety should be considered for performance improvement of a perilla cleaner and stone separator. Therefore the cleaner and stone separator using a sieve could be improved by the application of a detachable sieve or by using equipment such as a 2 - 3 stage sieve and regulating the slope. Moreover, because differences in the terminal velocity occur due to the differences in the size and thousand-grain weight according to the perilla variety, a blower with an adjustable fan speed was considered for the design of the improved cleaner. Additionally, it was shown that the length of perilla has the greatest correlation based on a comparison of the coefficients of the other characteristics. Accordingly, the length of perilla could be used as a major factor for the fine adjustment and parts replacement of the device. These results can be used as basic data for a high-quality, high-efficiency perilla cleaner and stone separator. In the future, the development of the machine and follow-up studies based on the basic data are needed to determine the optimized operating conditions and mechanism of action.

• Journal of IKEEE
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• v.24 no.4
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• pp.1004-1010
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• 2020

This paper proposes an initial rotor magnetic pole detection method for single-phase permanent magnet synchronous motors. The target motor cannot obtain position information based on the back emf in the low speed and stop state. Therefore, an open loop starting process is required, and in this process, initial rotor position information for low current and soft start is need. The proposed initial rotor magnetic pole detection algorithm considers the effect of asymmetric air- gap and magnetic flux. In addition, the high-frequency voltage signal injection and the offset voltage for accurate detection is used. As a result, the permanent magnet poles are is determined by acquiring the maximum value of the induced current using the virtual dq-axis.

• Korean Journal of Metals and Materials
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• v.46 no.11
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• pp.737-746
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• 2008

Mechanisms of sticking phenomena occurring during hot rolling of a modified STS 430J1L ferritic stainless steel have been investigated in this study by using a pilot-plant-scale rolling machine. As the rolling pass proceeds, the Fe-Cr oxide layer formed in a reheating furnace is destroyed, and the destroyed oxides penetrate into the rolled steel to form a thin oxide layer on the surface region. The sticking does not occur on the surface region containing oxides, whereas it occurs on the surface region without oxides by the separation of the rolled steel at high temperatures. This indicates that the resistance to sticking increases by the increase in the surface hardness when a considerable amount of oxides are formed on the surface region, and that the sticking can be evaluated by the volume fraction and distribution of oxides formed on the surface region. The lubrication and the increase of the rolling speed and rolling temperature beneficially affect to the resistance to sticking because they accelerate the formation of oxides on the steel surface region. In order to prevent or minimize the sticking, thus, it is suggested to increase the thickness of the oxide layer formed in the reheating furnace and to homogeneously distribute oxides along the surface region by controlling the hot-rolling process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.1-6
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• 2021

Most of the match-3 puzzle games supports automatic play using the MCTS algorithm. However, implementing reinforcement learning agents is not an easy job because it requires both the knowledge of machine learning and the way of complex interactions within the development environment. This study proposes a method in which we can easily design reinforcement learning agents and implement game play agents by applying PPO(Proximal Policy Optimization) algorithms. And we could identify the performance was increased about 44% than the conventional method. The tools we used are the Unity 3D game engine and Unity ML SDK. The experimental result shows that agents became to learn game rules and make better strategic decisions as experiments go on. On average, the puzzle gameplay agents implemented in this study played puzzle games better than normal people. It is expected that the designed agent could be used to speed up the game level design process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.627-632
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• 2021

ITL (information-theoretic learning) has been applied successfully to adaptive signal processing and machine learning applications, but there are difficulties in deciding the kernel size, which has a great impact on the system performance. The correntropy algorithm, one of the ITL methods, has superior properties of impulsive-noise robustness and channel-distortion compensation. On the other hand, it is also sensitive to the kernel sizes that can lead to system instability. In this paper, considering the sensitivity of the kernel size cubed in the denominator of the cost function slope, a new adaptive kernel estimation method using the rate of change in error power in respect to the kernel size variation is proposed for the correntropy algorithm. In a distortion-compensation experiment for impulsive-noise and multipath-distorted channel, the performance of the proposed kernel-adjusted correntropy algorithm was examined. The proposed method shows a two times faster convergence speed than the conventional algorithm with a fixed kernel size. In addition, the proposed algorithm converged appropriately for kernel sizes ranging from 2.0 to 6.0. Hence, the proposed method has a wide acceptable margin of initial kernel sizes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.700-705
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• 2020

Generative adversarial networks are methods of generating images by opposing two neural networks. When generating the image, randomly generated noise is rearranged to generate the image. The image generated by this method is not generated well depending on the noise, and it is difficult to generate a proper image when the number of pixels of the image is small In addition, the speed and size of data accumulation in data classification increases, and there are many difficulties in labeling them. In this paper, to solve this problem, we propose a technique to generate noise based on random noise using real data. Since the proposed system generates an image based on the existing image, it is confirmed that it is possible to generate a more natural image, and if it is used for learning, it shows a higher hit rate than the existing method using the hostile neural network respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.41-47
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• 2020

As modern industries are highly being developed, it is required that mechanical parts have to be manufactured with a high precision. In order to have precise parts, error-free designs have to be done before manufacturing with accuracy. For this intention being fulfilled, a mechanical analysis is essential for design proof. Nowadays, FEM simulation is a popular tool for verifying a machine design. In this paper, an impeller, being utilized in a compressor or an oil mixer as an actuator, is studied for an evaluation. The purpose of this study is to present a safety of an impeller for a proof of its mechanical stability. A static analysis for stress, strain, and deformation within a regular usage is examined. This simulation test shows 357.26×106 Pa for maximum equivalent stress and 0.207mm for total deformation. A fatigue test is carried to provide durability and its result shows that minimum safety factor is 3.2889, which guarantees that it runs without a fatigue failure in 106 cycles. The natural frequencies for the impeller is ranged from 228.09Hz to 1,253.6Hz for the 1st to the 6th mode. Total deformations at these natural frequencies are shown from 6.84mm to 12.631mm. Furthermore, Campbell diagram reveals that a critical speed is not found throughout regular rotational speeds. From the test results for the analysis, this paper concludes that the suggested impeller is proved for its mechanical safety and good to utilize at industries.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.471-476
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• 2021

Soft magnetic composite (SMC) materials based on powder metallurgy have a number of advantages over the conventional electrical steel sheets commonly used in electric machines. Thus, technologies related to these materials have shown significant improvement in recent years. In general, SMCs are magnetically isotropic owing to the shape of the powder, which makes them suitable for the construction of electric machines with three-dimensional flux and complex structures. However, the materials with isotropic magnetic properties (such as SMCs) have complex vector hysteresis; thus, it is very difficult to predict accurate loss properties. Therefore, we manufactured ring-type specimens of electrical steel sheets and SMC, which analyzed their magnetic properties according to the specimen size, and performed the electromagnetic field analysis of a high-speed permanent magnet (PM) motor driven at 800 Hz or higher using the measured magnetic information to compare the core loss of the motor. The reliability of this paper has been verified by measuring the efficiency after manufacturing the motor.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.