• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• Informatization Policy
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• v.29 no.3
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• pp.26-47
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• 2022

"The certification of compliance of new products of industrial convergence" (hereinafter referred to as "certification of compliance") is a legal certification system in accordance with the Industrial Convergence Promotion Act through which a convergence new product can be officially certified without legislation when the certification standards applicable to the product are not yet provided. Unlike other certification systems, the certification of compliance is characterized by the role of resolving the certification difficulties driven by the regulatory lag of convergence new products. Nevertheless, studies that analyzed the certification of compliance in detail from the viewpoint of regulatory improvement were surprisingly rare. Through the sequential matching of the steps of certification of compliance with the process from the occurrence of a regulatory problem to resolution, our study provided clear understanding as to how the regulatory lag could be reduced by the procedure for certification of compliance. Furthermore, we divided the perspective on regulatory lag management into quantitative and qualitative, and the structures and practices of certification of compliance were then analyzed from the two perspectives. By doing this, the present study emphasized that the fundamental reason the certification of compliance could effectively solve the regulatory lag problem of convergence new products was not only the quantitative elements such as legal deadlines for each step but also several qualitative approaches to securing the quality of every stage.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.805-811
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• 2012

The reliability based design optimization (RBDO) approach requires high computing cost to consider uncertainties. In order to reduce the design cost, the single loop single vector (SLSV) approach has been developed for RBDO. This method can reduce the cost in calculating deign sensitivity by elimination of the nested optimization process. However, this process causes the increment of the instability or inaccuracy of the method according to the problem characteristics. Therefore, the method may not give accurate solution or the robustness of the solution is not guaranteed. Especially, when the function is concave, the process frequently diverges. In this research, the concept of the conjugate gradient method for unconstrained optimization is utilized to develop a new single loop single vector method. The conjugate gradient is calculated with gradient directions at the most probable points (MPP) of previous cycles. Mathematical examples are solved for the verification of the proposed method. The numeri cal performances of the obtained results are compared to those of other RBDO methods. The SLSV approach using conjugate gradient is not greatly influenced by the problem characteristics and improves its convergence capability.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.57-65
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• 2015

Manufacturing optimization problem is to find the optimal solution under satisfying various and complicated constraints with the design variables of nonlinear types. To achieve the objective, this paper proposes a hybrid approach. The proposed hybrid approach is consist of genetic algorithm(GA), cuckoo search(CS) and hill climbing method(HCM). First, the GA is used for global search. Secondly, the CS is adapted to overcome the weakness of GA search. Lastly, the HCM is applied to search precisely the convergence space after the GA and CS search. In experimental comparison, various types of manufacturing optimization problems are used for comparing the efficiency between the proposed hybrid approach and other conventional competing approaches using various measures of performance. The experimental result shows that the proposed hybrid approach outperforms the other conventional competing approaches.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.302-305
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• 2005

Recently, many methods to analyze signal have been proposed and representative methods are the Fourier transform and wavelet transform. In these methods, the Fourier transform represents signal with combination cosine and sine at all locations in the frequency domain. However, it doesn't provide time information that particular frequency occurs in signal and denpends on only the global feature of the signal. So, to improve these points the wavelet transform which is capable of multiresolution analysis has been applied to many fields such as speech processing, image processing and computer vision. And the wavelet transform, which uses changing window according to scale parameter, presents time-frequency localization. In this paper, we proposed a new approach using a wavelet of cosine and sine type and analyzed features of signal in a limited point of frequency-time plane.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.1
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• pp.27-34
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• 2014

This paper proposes a new approach to the designed of visual feedback control system based on visual servoing method. The main focus of this paper is presented how it is effective to use many features for improving the accuracy of the visual feedback control of industrial articulated robot for assembling and inspection of parts. Some rank conditions, which relate the image Jacobian, and the control performance are derived. It is also proven that the accuracy is improved by increasing the number of features. The effectiveness of redundant features is verified by the real time experiments on a SCARA type robot(FARA) made in samsung electronics company.

• Journal of the Korea Convergence Society
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• v.5 no.2
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• pp.25-32
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• 2014

Indian economy growth changed due Indian business operational approach with convergent technologies. The current economy growth supported by Information Technology Development and Information Technology Enabled Service. As part of the Information technology enabled upcoming Knowledge processing outsourcing provide huge opportunities to develop Indian business with our human resources. The KPO business sector basis and frame work can be achieve through Development Dynamic initiatives. This paper discuss about the basis of KPO and Dynamic Development Initiative frame work strategic compact components and its characters.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.284-295
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• 2003

The servo tracks of disk drives are constructed at the time of manufacture with the equipment of servo track writer. Because of the imperfection of servo track writer, disk vibrations and head fluctuations during servo track writing process, the constructed servo tracks might deviate from perfect circles and take eccentric shapes. The servo track writing error should be corrected because it might cause interference with adjacent tracks and irrecoverable operation error of disk drives. The servo track writing error is repeated every disk rotation and so is periodic time function. In this paper, we propose a new correction algorithm of servo track writing error based on iterative teaming approach. Our correction algorithm can learn iteratively the servo track writing error as accurately as is desired. Furthermore, our algorithm is robust to system model errors, is computationally simple, and has fast convergence rate. In order to demonstrate the generality and practical use of our work, we present the convergence analysis of our correction algorithm and some simulation results.

• Journal of Institute of Convergence Technology
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• v.10 no.1
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• pp.7-12
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• 2020

We describe a efficient surface reconstruction method that reconstructs a 3D manifold polygonal mesh approximately passing through a set of 3D oriented points. Our algorithm includes 3D convex hull, octree data structure, signed distance function (SDF), and marching cubes. The 3D convex hull provides us with a fast computation of SDF, octree structure allows us to compute a minimal distance for SDF, and marching cubes lead to iso-surface generation with SDF. Our approach gives us flexibility in the choice of the resolution of the reconstructed surface, and it also enables to use on low-level PCs with minimal peak memory usage. Experimenting with publicly available scan data shows that we can reconstruct a polygonal mesh from point cloud of sizes varying from 10,000 ~ 1,000,000 in about 1~60 seconds.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.