• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.4
• /
• pp.79-86
• /
• 2009

In this paper, we propose a new clustering scheme that provides optimal data aggregation effect and reduces energy consumption of nodes by considering the density of nodes when forming clusters. Since the size of the cluster is determined to ensure optimal data aggregation rate, our scheme reduces transmission range and minimizes interference between clusters. Moreover, by clustering using locally adjacent nodes and aggregating data received from cluster members, we reduce energy consumption of nodes. Through simulation, we confirmed that energy consumption of the whole network is minimized and the sensor network life-time is extended. Moreover, we show that the proposed clustering scheme improves the performance of network compared to previous LEACH clustering scheme.

• Journal of Photoscience
• /
• v.9 no.2
• /
• pp.66-69
• /
• 2002

Zinc chlorin 1 possessing tertiary 3$^1$_hydroxy and 13$^1$-oxo groups was synthesized as a model for the antenna chlorophylls of photosynthetic green bacteria. Self-aggregation of 1 in nonpolar organic solvents was examined and compared to 2 and 3 possessing a secondary and primary 3$^1$_hydroxy group, respectively. Zinc chlorin 1 self-aggregated in I％(v/v) CH$_2$Cl$_2$-hexane to form oligomers and showed a red-shifted Qy maximum at 704 nm compared to the monomer (648 nm in CH$_2$CI2$_2$). This red-shift is larger than that of 3$^1$S-2 (648 to 697 nm) and comparable to that of3$^1$R-2 (648 to 705 nm), but smaller than that of 1 (648 to 740 nm), indicating that while a single 3$^1$-methyl group (primary to secondary OH) suppressed tight and/or extended aggregation, the additional 3$^1$-methyl group (secondary to tertiary OH) did not further suppress aggregation. The relative stability of the aggregates was in the order 3> 3$^1$R-2∼ 1 > 3$^1$S-2 as determined by visible spectral analyses. Molecular modeling calculations on oligomers of zinc chlorins 1, 3$^1$ R-2 and 3 gave similar well-ordered energy-minimized structures, while 3 stacked more tightly than 3$^1$ R- 2 and 1. In contrast, 3$^1$S-2 gave a relatively disordered (twisted) structure. The calculated oligomeric structures could explain the visible spectral data of 1-3 in nonpolar organic solvents. Moreover, self- aggregation of synthetic zinc 13$^1$_oxo-hlorins 4-6 possessing a 2-hydroxyethyl, 3-hydroxypropyl and 3- hydroxy-I-propenyl group at the 3-position in nonpolar organic solvents was discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.701-720
• /
• 2023

Aerial objects are more challenging to segment than normal objects, which are usually smaller and have less textural detail. In the process of segmentation, target objects are easily omitted and misdetected, which is problematic. To alleviate these issues, we propose local aggregation feature pyramid networks (LAFPNs) and pyramid integrated context modules (PICMs) for aerial object segmentation. First, using an LAFPN, while strengthening the deep features, the extent to which low-level features interfere with high-level features is reduced, and numerous dense and small aerial targets are prevented from being mistakenly detected as a whole. Second, the PICM uses global information to guide local features, which enhances the network's comprehensive understanding of an entire image and reduces the missed detection of small aerial objects due to insufficient texture information. We evaluate our network with the MS COCO dataset using three categories: airplanes, birds, and kites. Compared with Mask R-CNN, our network achieves performance improvements of 1.7%, 4.9%, and 7.7% in terms of the AP metrics for the three categories. Without pretraining or any postprocessing, the segmentation performance of our network for aerial objects is superior to that of several recent methods based on classic algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.11
• /
• pp.2187-2192
• /
• 2016

A data-centric routing protocol considering a data aggregation technique at relay nodes is required to increase the lifetime of wireless sensor networks. An energy-efficient data-centric routing algorithm is proposed by considering a tradeoff between acquisition time and energy consumption in the wireless sensor network. First, the proposed routing scheme decides the sink node among all sensor nodes in order to minimize the maximum distance between them. Then, the proposed routing extends its tree structure in a way to minimize the link cost between the connected nodes for reducing energy consumption while minimizing the maximum distance between sensor nodes and a sink node for rapid information gathering. Simulation results show that the proposed data-centric routing algorithm has short information acquisition time and low energy consumption; thus, it achieves high energy efficiency in the wireless sensor network compared to conventional routing algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.2
• /
• pp.1-11
• /
• 2020

As an important basic building block of the smart grid environment, smart meter provides real-time electricity consumption information to the utility. However, ensuring information security and privacy in the smart meter data aggregation process is a non-trivial task. Even though the secure data aggregation for the smart meter has been a lot of attention from both academic and industry researchers in recent years, most of these studies are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter system. Recently, blockchains or distributed ledgers are an emerging technology that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. In particular, blockchains are identified as having the potential to bring significant benefits and innovation for the electricity consumption network. This study suggests a distributed, privacy-preserving, and simple secure smart meter data aggregation system, backed up by Blockchain technology. Smart meter data are aggregated and verified by a hierarchical Merkle tree, in which the consensus protocol is supported by the practical Byzantine fault tolerance algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3128-3149
• /
• 2018

A number of effective methods for multiple-attribute group decision making (MAGDM) with interval-valued intuitionistic fuzzy numbers (IVIFNs) have been proposed in recent years. However, the different methods frequently yield different, even sometimes contradictory, results for the same problem. In this paper a novel criterion to determine the advantages and disadvantages of different methods is proposed. First, the decision-making process is divided into three parts: translation of experts' preferences, aggregation of experts' opinions, and comparison of the alternatives. Experts' preferences aggregation is considered the core step, and the quality of the collective matrix is considered the most important evaluation index for the aggregation methods. Then, methods to calculate the similarity measure, correlation, correlation coefficient, and energy of the intuitionistic fuzzy matrices are proposed, which are employed to evaluate the collective matrix. Thus, the optimal method can be selected by comparing the collective matrices when all the methods yield different results. Finally, a novel approach for aggregating experts' preferences with IVIFN is presented. In this approach, experts' preferences are mapped as points into two-dimensional planes, with the plant growth simulation algorithm (PGSA) being employed to calculate the optimal rally points, which are inversely mapped to IVIFNs to establish the collective matrix. In the study, four different methods are used to address one example problem to illustrate the feasibility and effectiveness of the proposed approach.

• Bulletin of the Korean Chemical Society
• /
• v.24 no.5
• /
• pp.547-551
• /
• 2003

In the present work, the interaction of three water soluble porphyrins, tetra(p-trimethyle) ammonium phenyl porphyrin iodide (TAPP) as a cationic porphyrin, tetra sodium meso-tetrakis (p-sulphonato phenyle) porphyrin (TSPP) as an anionic porphyrin and manganese tetrakis (p-sulphonato phenyl) porphinato acetate (MnTSPP) as a metal porphyrin, with histone H₂B have been studied by isothermal titration microcalorimetry at 8 mM phosphate buffer, pH 6.8 and 27 °C. The values of binding constant, entropy, enthalpy and Gibbs free energy changes for binding of the first MnTSPP, and first and second TSPP and TAPP molecules were estimated from microcalorimetric data analysis. The results represent that the process is both entropy and enthalpy driven and histone induces self-aggregation of the porphyrins. The results indicate that both columbic and hydrophobic interactions act as self-aggregation driving forces for the formation of aggregates around histone.

• Nuclear Engineering and Technology
• /
• v.56 no.2
• /
• pp.624-635
• /
• 2024

In two-fluid simulations of flow boiling, the modeling of the mean bubble diameter is a key parameter in the closure relations governing the intefacial transfer of mass, momentum, and energy. Monodispersed approach proved to be insufficient to describe the significant variation in bubble size during flow boiling in a heated pipe. A population balance model (PBM) has been employed to address these shortcomings. During nucleate boiling, vapor bubbles of a certain size are formed on the heated wall, detach and migrate into the bulk flow. These bubbles then grow, shrink or disintegrate by evaporation, condensation, breakage and aggregation. In this study, a parametric analysis of the PBM aggregation and breakage models has been performed to investigate their effect on the radial distribution of the mean bubble diameter and vapor volume fraction. The simulation results are compared with the DEBORA experiments (Garnier et al., 2001). In addition, the influence of PBM parameters on the local distribution of individual bubble size groups was also studied. The results have shown that the modeling of aggregation process has the largest influence on the results and is mainly dictated by the collisions due to flow turbulence.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.229-256
• /
• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.07a
• /
• pp.415-417
• /
• 2005

Core operations (e.9. TDMA scheduler, synchronized sleep period, data aggregation) of many proposed protocols for different layer of sensor network necessitate clock synchronization. Our Paper mingles the scheme of dynamic clustering and diffusion based asynchronous averaging algorithm for clock synchronization in sensor network. Our proposed algorithm takes the advantage of dynamic clustering and then applies asynchronous averaging algorithm for synchronization to reduce number of rounds and operations required for converging time which in turn save energy significantly than energy required in diffusion based asynchronous averaging algorithm.