• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2732-2749
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• 2023

Device-to-Device (D2D) communication has received increasing attention and been studied extensively thanks to its advantages in improving spectral efficiency and energy efficiency of cellular networks. This paper proposes a novel relay selection algorithm for multi-hop full-duplex D2D communications underlaying cellular networks. By selecting the relay of each hop in a reverse manner, the proposed algorithm reduces the heavy signaling overhead that traditional relay selection algorithms introduce. In addition, the social domain information of mobile terminals is taken into consideration and its influence on the performance of D2D communications studied, which is found significant enough not to be overlooked. Moreover, simulations show that the proposed algorithm, in absence of social relationship information, improves data throughput by around 70% and 7% and energy efficiency by 64% and 6%, compared with two benchmark algorithms, when D2D distance is 260 meters. In a more practical implementation considering social relationship information, although the proposed algorithm naturally achieves less throughput, it substantially increases the energy efficiency than the benchmarks.

• ETRI Journal
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• v.45 no.5
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• Advances in nano research
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• v.16 no.5
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• pp.445-458
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• 2024

The objective of this paper is to present free vibration and static stability analyses of rotating composite beams reinforced with carbon nanotubes (CNTs) under uniform thermal loads. Beam structural equations and CNT-reinforced composite (CNTRC) beam formulations are derived based on Timoshenko beam theory (TBT). The temperature-dependent properties of the beam material, such as the elastic modulus, shear modulus, and material density, are assumed to vary over the thickness according to the rule of mixture. The beam material is modeled as a mixture of single-walled carbon nanotubes (SWCNTs) in an isotropic matrix. The SWCNTs are aligned and distributed in the isotropic matrix with different patterns of reinforcement, namely the UD (uniform), FG-O, FG-V, FG- Λ and FG-X distributions, where FG-V and FG- Λ are asymmetric patterns. Numerical examples are presented to illustrate the effects of several essential parameters, including the rotational speed, hub radius, effective material properties, slenderness ratio, boundary conditions, thermal force, and moments due to temperature variation. To the best of the authors' knowledge, this study represents the first attempt at the finite element modeling of rotating CNTRC Timoshenko beams under a thermal environment. The results are presented in tables and figures for both symmetric and asymmetric distribution patterns, and can be used as benchmarks for further validation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.12
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• pp.61-70
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• 2005

In this paper, we propose an efficient global placement algorithm which is an enhanced version of Hybrid Placer$^{[25]}$, a standard cell placement tool, which uses a middle-down approach. Combining techniques used in the well-known partitioner hMETIS and the RBLS(Relaxation Based Local Search) in Hybrid Placer improves the quality of global placements. Partitioning techniques of hMETIS is applied in a top-down manner and RBLS is used in each level of the top-down hierarchy to improve the global placement. The proposed new approach resolves the problem that Hybrid Placer seriously depends on initial placements and it speeds up without deteriorating the placement quality. Experimental results prove that solutions generated by the proposed method on the MCNC benchmarks are comparable to those by FengShui which is a well known placement tool. Compared to the results of the original Hybrid Placer, new method is 5 times faster on average and shows improvement on bigger circuits.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.11
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• pp.56-62
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• 2008

Modern microprocessors must deliver high application performance, while the design process should not subordinate power. In terms of performance and power tradeoff, the instructions window is particularly important. This is because a large instruction window leads to achieve high performance. However, naive scaling conventional instruction window can severely affect the complexity and power consumption. This paper explores an architecture level approach to reduce power dissipation. We propose a low power issue logic with an efficient tag translation. The direct lookup table (DTL) issue logic eliminates the associative wake-up of conventional instruction window. The tag translation scheme deals with data dependencies and resource conflicts by using bit-vector based structure. Experimental results show that, for SPEC2000 benchmarks, the proposed design reduces power consumption by 24.45% on average over conventional approach.

• Journal of KIISE
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• v.44 no.3
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• pp.232-238
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• 2017

Application response time is critical to end-user response time in Android smartphones. Due to the plentiful resources of recent smartphones, storage I/O response time becomes a major key factor in application response time. However, existing storage I/O trace tools for Android and Linux give limited information only for a specific I/O layer which makes it difficult to combine I/O information from different I/O layers, because not helpful for application developer and researchers. In this paper, we propose a novel storage I/O trace tool for Android, called AIOPro (Android I/O profiler). It traces storage I/O from application - Android platform - system call - virtual file system - native file system - page cache - block layer - SCSI layer and device driver. It then combines the storage I/O information from I/O layers by linking them with file information and physical address. Our evaluations of real smartphone usage scenarios and benchmarks show that AIOPro can track storage I/O information from all I/O layers without any data loss under 0.1% system overheads.

• Journal of Environmental Health Sciences
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• v.29 no.1
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• pp.19-27
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• 2003

Coliforms is currently being used as the standard of environmental water qualify to evaluate the level of source water quality especially condition of fecal contamination. However, not properly applied to water quality management. So in this study, in addition to Coliforms, fecal contamination indicator bacteria turk at Feral Coliforms(FC), E. coli, Fecal streptococci(FS), Clostridium and environmental parameters related with it's distribution were investigated on a monthly basis in 6 water intakes of Han River. The mean of BOD, DO, SS and pH, benchmarks of source water management were maintained the second grade of environmental water quality standard applied to Han River but Coliforms exceeded it. Distribution of Coliforms ranged from 1.0×10¹ to 2.7 10/sup 5/ CFU/ml, FC ranged from ND to 5.3×10¹ CFU/ml, E. coli ranged from ND to 9.2×10¹ CFU/ml, FS ranged from ND to 2.5×10¹CFU/ml, they were steepy rise on July and August in common when rainfalls was heavy and water temperature was high, but Clostridium perfringens ranged from 1.7×10¹to 1.7×10¹CFU/ml not fluctuate by month. Statistical analysis of sampling data showed that most significant correlations occurred among FC and Coliforms(r = 0.840), E. coli(r = 0.792), FS(r = 0.687) and environmental parameters(temperature, turbidity, SS, rotor were all r > 0.60) while no significant correlation was observed between ammonia generally recognized fecal contamination indicator and bacteria. Identification of the coliforms showed that Enterobacter, Klebsiella, Citrobacter were comprised of 32%, 24%, 16% respectively, and E. coli were 7% of it. while E. coli was made up 85.9% of FC. The mean value of FC/Coliforms ratio, 5.2(0.1-42) were higher in Amsa, Guui than Jayang. Fecal coliforms, as those are able to reflect more particularly the extent of the fecal contamination, were considered useful in deciding the level of water treatment while monitoring the fecal contamination from the source of water supply. Therefore, it is expected that the water quality is going to be managed more efficiently by using fecal coliforms supplementarily to total coliforms which are current standard item of water-quality environment.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.1
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• pp.61-66
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• 2011

The performance of microchannel PCHE (Printed Circuit Heat Exchanger) is superior to that of other existing commercial heat exchangers. Further, it is also more efficient than other heat exchangers. Various microchannels, whose shapes are straight (I), Wavy, Beehive, Surf, I-Wavy, I-Beehive, or I-Surf, are computationally modeled in this study. The counter-flow arrangement is used, and the flow characteristics, heat transfer, and pressure drop in the microchannels under various mass flow rate conditions are investigated. The results for I microchannel is chosen as the benchmarks and is compared with those of newly proposed microchannels. It is found that the surf-shaped microchannel is most efficient in improving the overall performance of a PCHE.