• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Progress in Medical Physics
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• v.25 no.3
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• pp.185-192
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• 2014

The purpose of this study is to evaluate the results for the quality assurance through a statistical analysis on the output characteristics of linear accelerators belonging to Yeungnam University Medical Center by using the Shewhart-type chart, Exponentially weighted moving average chart (EWMA) chart, and process capability indices $C_p$ and $C_{pk}$. To achieve this, we used the output values measured using respective treatment devices (21EX, 21EX-S, and Novalis Tx) by medical physicists every month from September, 2012 to April, 2014. The output characteristics of treatment devices followed the IAEA TRS-398 guidelines, and the measurements included photon beams of 6 MV, 10 MV, and 15 MV and electron beams of 4 MeV, 6 MeV, 9 MeV, 12 MeV, 16MeV, and 20 MeV. The statistical analysis was done for the output characteristics measured, and was corrected every month. The width of control limit of weighting factors and measurement values were calculated as ${\lambda}=0.10$ and L=2.703, respectively; and the process capability indices $C_p$ and $C_{pk}$ were greater than or equal to 1 for all energies of the linear accelerators (21EX, 21EX-S, and Novalis Tx). Measured values of output doses with drastic and minor changes were found through the Shewhart-type chart and EWMA chart, respectively. The process capability indices $C_p$ and $C_{pk}$ of the treatment devices in our institution were, respectively, 2.384 and 2.136 for 21EX, 1.917 and 1.682 for 21EX-S, and 2.895 and 2.473 for Novalis Tx, proving that Novalis Tx has the most stable and accurate output characteristics.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.11
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• pp.1018-1024
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• 2010

The printer noise consists of the noise of the various components and parts such as motor, fan and solenoid. And the human's printing sound recognition shows various aspects when the printer starts to print papers because the components operate at the same time. Especially, printers are usually installed in the quiet office room. Therefore the printing noise is related to its competitiveness in the market. The importance of the printer sound qualities is increasing and it is necessary to develop the sound quality evaluation system, so it is a key point to identify the noise source of the printer and develop the sound quality index to each component. By using this evaluation system, it is possible to evaluate the sound quality of a prototype printer compared to the already existing one. In this paper, the printer sound quality evaluation system was developed by the following steps. Firstly, the signal processing method was applied to the recorded printing sound to identity and split the noise of components. Secondly, the MLR(multiple linear regression) method and the psychoacoustics were used to develop the sound quality index. Finally, the improvement of the printer sound quality is possible by using the result of the MLR and the path analysis. The output of this research will be applied to the development of a new printer.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1443-1448
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• 2006

The reduction of the vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. Even though the HVAC noise is not louder than overall noise level, it clearly affects subjective perception in the way of making a diver become nervous or annoyed. Therefore, these days a vehicle engineer takes aim at developing sound quality as well as reduction of noise. In this paper, we acquired noises in the HVAC from many vehicles. Through the objective and subjective sound quality evaluation with acquiring noises caused by the vehicle HVAC system, the simple and multiple regression models were obtained for the subjective evaluation 'Pleasant' using the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Furthermore, the neural network model were obtained using three inputs(loudness, sharpness and roughness) of the sound quality metrics and one output(subjective 'Pleasant'). And then the models were compared with correlations between sound quality index outputs and hearing test results for 'Pleasant'. As a result of application of the sound quality index, the neural network was verified with the largest correlation of the sound quality index.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.409-416
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• 2009

This paper presents a tool called Quality-Cost optimization system (QCOS), which integrates Multi-Objective Genetic Algorithm (MOGA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the construction owner who has an experience of curtain-wall project, the architects who are the independent assessor, and the contractors who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between CRs and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input to compute the Owner Satisfaction (OS) and Contractor Satisfaction (CS). MOGA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using MOGA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input and the variability of QFD output. A case study demonstrates the system and verifies the system conformance.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.846-851
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• 2017

Piezoelectricity is the capability of a piezoelectric material to change mechanical energy into electrical energy. The determination of electrical and mechanical properties plays a significant role in characterizing the piezoelectric material. The energy losses characteristics of piezoelectric material can be described by mechanical quality factor. In this paper, the output voltage and mechanical quality factor of Lead Zirconate Titanate (PZT-4A) piezoelectric material is determined under various resistance and loading conditions by using the test setup. The commercial FEM software ABAQUS is used to analyze the performance of piezoelectric material under static loading conditions. It is observed that these properties affect the performance of a material particularly in the designing of smart structures. The experimental results are partially compared to the simulation values.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.109-118
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• 2005

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.539-541
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• 2009

A severely cold weather condition of King Sejong Station, Antarctica becomes a very severe condition for an installation/operation of wind generation system. When the existing wind generation system works, it may cause a damage and destruction of wind generation system and can bring about big problems in terms of the power quality. Accordingly, it is essential to obtain technologies for the installation and operation of small wind generation system for the polar region's wind generation, and to assess and demonstrate the performance in the severely-cold environment and the polar wind generation system's development, supplementation, alteration. Also, as the available power of King Sejong Station, Antarctica, the diesel generator has been mainly used, and the wind generator has been used in the hybrid form. Wind generation and diesel generation has the different load following control each other. In the wind generation, the generated power very rapidly changes according to the change of the velocity of the wind. On the other hand, the diesel generation shows very gentle change in the velocity of output. Therefore, the study is intended to analyze the 10kw small wind generator-diesel generator's power quality of King Sejong Station, Antarctica, which is the hybrid system installation area.

• Journal of Computing Science and Engineering
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• v.7 no.3
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• pp.168-176
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• 2013

Long-term evolution (LTE) is emerging as a major candidate for 4G cellular networks to satisfy the increasing demands for mobile broadband services, particularly multimedia delivery. Multiple-input multiple-output (MIMO) technology combined with orthogonal frequency division multiple access and more efficient modulation/coding schemes (MCS) are key physical layer technologies in LTE networks. However, in order to fully utilize the benefits of the advances in physical layer technologies, the MIMO configuration and MCS need to be dynamically adjusted to derive the promised gains of 4G at the application level. This paper provides a performance evaluation of video traffic with variations in the physical layer transmission parameters to suit the varying channel conditions. A quantitative analysis is provided using the perceived video quality as a video quality measure (evaluated using no-reference blocking and blurring metrics), as well as transmission delay. Experiments are performed to measure the performance with changes in modulation and code rates in poor and good channel conditions. We discuss how an adaptive scheme can optimize the performance over a varying channel.

• Journal of Power Electronics
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• v.12 no.4
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• pp.664-676
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• 2012

A phase shift algorithm based on the closed-loop control of dc-link voltage implemented on a series active voltage quality regulator (AVQR) is proposed in this paper. To avoid pumping-up the dc-link voltage, a general phase shift compensation strategy is applied. The relationships among the operation variables are discussed in detail, which is very important for guiding the design of both the main circuit and the control system. Then on the basis of an investigation of the dc-link voltage pumping-up from viewpoint of the active power flow, a novel phase shift control method based on the closed-loop of the dc-link voltage is proposed. This method can adjust the phase of the output voltage gradually and automatically according to the dc-link voltage variation without introducing a phase jump. The effectiveness of the proposed strategy is verified through simulations of a single-phase 5kVA prototype and laboratory experiments on both a single-phase 5kVA and a three-phase 15kVA prototype.