• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.4
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• pp.83-92
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• 2005

D-OCSP-KIS proposed by Koga and Sakurai not only reduces the number or OCSP Responder's certificate but also criers the certificate status validation about OCSP Responder to the client. Therefore, D-OCSP-KIS is an effective method that can reduce the communication cost, computational time and storage consumption in client, but it has some problems. In case an attacker accidentally acquires an OCSP Responder's session private key in a time period (e.g., one day), she can disguise as the OCSP Responder in the time period unless the OCSP Responder recognizes. She can offer the wrong response to the client using the hash value intercepted. And the server and user on I-commerce can have a serious confusion and damage. And the computation and releasing of hash chain can be a load to CA. Thus, we propose a method detecting immediately the exposure of an OCSP Responder's session private key and the abuse of hash value in D-OCSP-KIS.

• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1053-1060
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• 2021

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• Microbiology and Biotechnology Letters
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• v.49 no.1
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• pp.75-87
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• 2021

Type I Interferons (IFNα) are known for their role as biological anticancer agents owing to their cell-apoptosis inducing properties. Development of an appropriate, cost-effective host expression system is crucial for meeting the increasing demand for proteins. Therefore, this study aims to develop codon-optimized IFNα-2b in L. lactis NZ3900. These cells express extracellular protein using the NICE system and Usp₄₅ signal peptide. To validate the mature form of the expressed protein, the recombinant IFNα-2b was screened in a human colorectal cancer cell line using the cytotoxicity assay. The IFNα-2b was successfully cloned into the pNZ8148 vector, thereby generating recombinant L. lactis pNZ8148-SP_Usp45-IFNα-2b. The computational analysis of codon-optimized IFNα-2b revealed no mutation and amino acid changes; additionally, the codon-optimized IFNα-2b showed 100% similarity with native human IFNα-2b, in the BLAST analysis. The partial size exclusion chromatography (SEC) of extracellular protein yielded a 19 kDa protein, which was further confirmed by its positive binding to anti-IFNα-2b in the western blot analysis. The crude protein and SEC-purified partial fraction showed IC₅₀ values of 33.22 ㎍/ml and 127.2 ㎍/ml, respectively, which indicated better activity than the metabolites of L. lactis NZ3900 (231.8 ㎍/ml). These values were also comparable with those of the regular anticancer drug tamoxifen (105.5 ㎍/ml). These results demonstrated L. lactis as a promising host system that functions by utilizing the pNZ8148 NICE system. Meanwhile, codon-optimized usage of the inserted gene increased the optimal protein expression levels, which could be beneficial for its large-scale production. Taken together, the recombinant L. lactis IFNα-2b is a potential alternative treatment for colorectal cancer. Furthermore, its activity was analyzed in the WiDr cell line, to assess its colorectal anticancer activities in vivo.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.183-192
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• 2022

With the growth of the packaging industry, demand on the packaging printing comes in various forms. Customers' orders are diversifying and the standards for quality are increasing. Offset printing is mainly used in the packaging printing since it is easy to print in large quantities. However, productivity of the offset printing decreases when printing various order. This is because it takes time to change colors for each printing unit. Therefore, scheduling that minimizes the color replacement time and shortens the overall makespan is required. By the existing manual method based on workers' experience or intuition, scheduling results may vary for workers and this uncertainty increase the production cost. In this study, we propose an automated scheduling method of parallel offset printing process for packaging printing. We decompose the original problem into assigning and sequencing orders, and ink arrangement for printing problems. Vehicle routing problem and assignment problem are applied to each part. Mixed integer programming is used to model the problem mathematically. But it needs a lot of computational time to solve as the size of the problem grows. So guided local search algorithm is used to solve the problem. Through actual data experiments, we reviewed our method's applicability and role in the field.

• Composites Research
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• v.36 no.3
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• pp.205-210
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• 2023

In this study, we developed a transfer learning framework based on homogenization data for efficient prediction of the effective mechanical properties and thermal conductivity of cellular foam structures. Mean-field homogenization (MFH) based on the Eshelby's tensor allows for efficient prediction of properties in porous structures including ellipsoidal inclusions, but accurately predicting the properties of cellular foam structures is challenging. On the other hand, finite element homogenization (FEH) is more accurate but comes with relatively high computational cost. In this paper, we propose a data-driven transfer learning framework that combines the advantages of mean-field homogenization and finite element homogenization. Specifically, we generate a large amount of mean-field homogenization data to build a pre-trained model, and then fine-tune it using a relatively small amount of finite element homogenization data. Numerical examples were conducted to validate the proposed framework and verify the accuracy of the analysis. The results of this study are expected to be applicable to the analysis of materials with various foam structures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.671-678
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• 2006

In this study, to describe vehicle-structure dynamic interaction phenomena with 1/4 vehicle model, nonlinear Hertzian contact spring and nonlinear contact damper are adopted. The external loads acting on 1/4 vehicle model are selfweight of vehicle and geometry information of running surface. The constraint equation on contact surface is implemented by the Penalty method with stabilization and the reaction from constraint violation. To describe pitching motion of various vehicles two types of the displacement constraint equations are exerted to connect between car bodies and between bogie frames, i.e., the rigid body connection and the rigid body connection with pin, respectively. For the time integration of dynamic equations of vehicles and structure Newmark time integration scheme is adopted. To reduce the error caused by inadequate time step size, adaptive time-stepping technique is also adopted. Thus, it is expected that more versatile dynamic interaction phenomena can be described by this approach and it can be applied to various railway dynamic problems with low computational cost.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5A
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• pp.861-872
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• 2006

The element free Galerkin method is extended by the local partition of unity method to model the cohesive cracks in two dimensional continuum. The shape function of a particle whose domain of influence is completely cut by a crack is enriched by the step enrichment function. If the domain of influence contains a crack tip inside, it is enriched by a branch enrichment function which does not have the LEFM stress singularity. The discrete equations are obtained directly from the standard Galerkin method since the enrichment is only for the displacement field, which satisfies the local partition of unity. Because only particles whose domains of influence are influenced by a crack are enriched, the system matrix is still sparse so that the increase of the computational cost is minimized. The condition for crack growth in dynamic problems is obtained from the material instability; when the acoustic tensor loses the positive definiteness, a cohesive crack is inserted to the point so as to change the continuum to a discontiuum. The crack speed is naturally obtained from the criterion. It is found that this method is more accurate and converges faster than the classical meshless methods which are based on the visibility concept. In this paper, several well-known static and dynamic problems were solved to verify the method.