• Journal of the Korean Data and Information Science Society
• /
• 제28권5호
• /
• pp.971-980
• /
• 2017

가변계수 회귀모형은 회귀계수의 동적변화를 모형화함으로써 종속변수와 입력변수의 관계에 대한 쉬운 해석이 가능하고 회귀계수의 변동성도 추정할 수 있는 장점을 지니고 있으므로, 여러 과학 분야에서 많은 주목을 받고 있다. 본 논문에서 입력변수와 출력변수의 오차를 효과적으로 고려한 가변계수 오차모형을 제안한다. 가변계수가 평활변수의 알려지지 않은 형태의 비선형함수이므로 이를 추정하기 위하여 커널 방법을 사용한다. 제안된 모형의 성능에 영향을 미치는 초모수의 최적값을 구하기 위하여 일반화 교차타당성 방법 또한 제안한다. 제안된 방법은 모의자료와 실제자료를 이용한 수치적 연구를 통하여 평가된다.

• 한국미생물·생명공학회지
• /
• 제48권3호
• /
• pp.358-365
• /
• 2020

An organic solvent- and bleach-stable protease-producing strain was isolated from a polluted river water sample and identified as Aeromonas veronii OB3 on the basis of biochemical properties (API 20E) and 16S rRNA sequence analysis. The strain was found to hyper-produce alkaline protease when cultivated on fish waste powder-based medium (HVSP, 4080 U/ml). The biochemical properties and compatibility of OB3 with several detergents and additives were studied. Maximum activity was observed at pH 9.0 and 60℃. The crude protease displayed outstanding stability to the investigated surfactants and oxidants, such as Tween 80, Triton X-100, and H₂O₂, and almost 36% residual activity when incubated with 1% SDS. Remarkably, the enzyme demonstrated considerable compatibility with commercial detergents, retaining more than 100% of its activity with Ariel and Tide (1 h, 40℃). Moreover, washing performance of Tide significantly improved by the supplementation of small amounts of OB3 crude protease. These properties suggest the potential use of this alkaline protease as a bio-additive in the detergent industry and other biotechnological processes such as peptide synthesis.

• 소성∙가공
• /
• 제29권2호
• /
• pp.89-96
• /
• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권3호
• /
• pp.1557-1569
• /
• 2017

In recent times, the Internet of Things (IoT) has rapidly emerged as one of the most influential information and communication technologies (ICT). The various constituents of the IoT together offer novel technological opportunities by facilitating the so-called "hyper-connected world." The fundamental tasks that need to be performed to provide such a function involve the transceiving, storing, and analyzing of digital data. However, it is challenging to handle voluminous data with IoT devices because such devices generally lack sufficient computational capability. In this study, we examine the IoT from the perspective of security and digital forensics. SQLite is a light-weight database management system (DBMS) used in many IoT applications that stores private information. This information can be used in digital forensics as evidence. However, it is difficult to obtain critical evidence from IoT devices because the digital data stored in these devices is frequently deleted or updated. To address this issue, we propose Schema Pattern-based Recovery (SPaRe), an SQLite recovery scheme that leverages the pattern of a database schema. In particular, SPaRe exhaustively explores an SQLite database file and identifies all schematic patterns of a database record. We implemented SPaRe on an iPhone 6 running iOS 7 in order to test its performance. The results confirmed that SPaRe recovers an SQLite record at a high recovery rate.

• 한국소음진동공학회논문집
• /
• 제19권11호
• /
• pp.1151-1157
• /
• 2009

Ensuring both product quality and reducing material cost are important issue for the design of the piping system of an air conditioner outdoor unit. This paper describes a shape optimization that achieves mass reduction of an air conditioner piping system while satisfying two design constraints on resonance avoidance and the maximum stress in the pipes. In order to obtain optimized design results with various analysis fields considered simultaneously, an automated multidisciplinary analysis system was constructed using PIAnO v.2.4, a commercial process integration and design optimization(PIDO) tool. As the first step of the automated analysis system, a finite element model is automatically generated corresponding to the specified shape of the pipes using a morphing technique included in HyperMesh. Then, the performance indices representing various design requirements (e.g. natural frequency, maximum stress and pipe mass) are obtained from the finite element analyses using appropriate computer-aided engineering(CAE) tools. A sequential approximate optimization(SAO) method was employed to effectively obtain the optimum design. As a result, the pipe mass was reduced by 18 % compared with that of an initial design while all the constraints were satisfied.

• 소성∙가공
• /
• 제21권8호
• /
• pp.499-505
• /
• 2012

The fuel cell is a very promising power generation system combining the benefits of extremely low emissions, high efficiency, ease of maintenance and durability. In order to promote the commercialization of fuel cells, a flexible forming process, in which a hyper-elastic rubber is adopted as a medium to transmit forming pressure, is suggested as an efficient and cost effective manufacturing method for fuel cell bipolar plates. In this study, the ability of this flexible forming process to produce the micro channel arrays on metallic bipolar plates was first demonstrated experimentally. Then, a finite element (FE) model was built and validated through comparisons between simulated and experimental results. The effects of key process parameters on the forming performance such as applied load and punch velocity were investigated. As a result, appropriate process parameter values allowing high dimensional accuracy without failure were suggested.

• 소성∙가공
• /
• 제29권4호
• /
• pp.218-228
• /
• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Design & Manufacturing
• /
• 제14권3호
• /
• pp.1-7
• /
• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• 제어로봇시스템학회논문지
• /
• 제14권2호
• /
• pp.184-190
• /
• 2008

This paper describes a message model based on XML (eXtensible Markup Language) to present real-time data from sensors and instruments at manufacturing processes for web service. HTML (Hyper Text Markup Language) is inadequate for describing real-time data from process control plants while it is suitable for displaying non-real-time multimedia data on web. For XML-based web service of process data, XML format for the data presentation was proposed after investigating data of various instruments at steel-making plants. Considering transmission delay inevitably caused from increased message length and processing delay from transformation of raw data into defined format, which was critical for operation of a real-time system, its performance was evaluated by simulation. In the simulation, we assumed two implementation models for conducting the transformation function. In one model, transformation was done at an SCC (Supervisory Control Computer) after receiving real-time data from instruments. In the other model, transformation had been carried out at instruments before the data were transmitted to the SCC. Various tests had been conducted under different conditions of offered loads and data lengths and their results were described.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 1999년도 추계종합학술대회
• /
• pp.193-198
• /
• 1999

In the 21th century, everyone feels that the multimedia system is close at hand in real life due to the rapid advance of the computer processing ability and high speed and high guality of communication services. Also the limited frequencies resource will be optimized due to rapid advances in digital video technology which is believed superior to analogue technology in information engineering. MEPG-2 has been introduced for broadcasting use such as digital TV Thus it features the high-definition and hyper-low bit rate. But, because of much throughput it has been implemented by high-priced private ASIC chip and is not in general use yet. But in this research, noticing the rapid enhancement of PC processor performance comparing with the price. MPEG-2 was developed by real time software MPEG-2 had been known impossible to implement with S/W, but the research proved the possibility of the S/W implementation and below are the pictures also in the research was improved 'Motion Vector and Compensation' Algorithm which requires the most operations and UT was made possible real time process. Multimedia Info Society has settled and accompanied by the rapid advance of image-processing technology and lots of standards.