Sang-Hyeon Park

Su Yeong Jo, Sanghyeon Park, Seungchan Ko et al.

The Saint-Venant torsion theory is a classical theory for analyzing the torsional behavior of structural components, and it remains critically important in modern computational design workflows. Conventional numerical methods, including the finite element method (FEM), typically rely on mesh-based approaches to obtain approximate solutions. However, these methods often require complex and computationally intensive techniques to overcome the limitations of approximation, leading to significant increases in computational cost. The objective of this study is to develop a series of novel numerical methods based on physics-informed neural networks (PINN) for solving the Saint-Venant torsion equations. Utilizing the expressive power and the automatic differentiation capability of neural networks, the PINN can solve partial differential equations (PDEs) along with boundary conditions without the need for intricate computational techniques. First, a PINN solver was developed to compute the torsional constant for bars with arbitrary cross-sectional geometries. This was followed by the development of a solver capable of handling cases with sharp geometric transitions; variable-scaling PINN (VS-PINN). Finally, a parametric PINN was constructed to address the limitations of conventional single-instance PINN. The results from all three solvers showed good agreement with reference solutions, demonstrating their accuracy and robustness. Each solver can be selectively utilized depending on the specific requirements of torsional behavior analysis.

Junmo Lee, Seongjun Kim, Sanghyeon Park et al.

Cryptocurrencies such as Bitcoin and Ethereum have made payment transactions possible without a trusted third party, but they have a scalability issue due to their consensus mechanisms. Payment networks have emerged to overcome this limitation by executing transactions outside of the blockchain, which is why these are referred to as off-chain transactions. In order to establish a payment channel between two users, the users lock their deposits in the blockchain, and then they can pay each other through the channel. Furthermore, payment networks support multi-hop payments that allow users to transfer their balances to other users who are connected to them via multiple channels. However, multi-hop payments are hard to be accomplished, as they are heavily dependent on routing users on a payment path from a sender to a receiver. Although routing hubs can make multi-hop payments more practical and efficient, they need a lot of collateral locked for a long period and have privacy issues in terms of payment history. We propose RouTEE, a secure payment routing hub that is fully feasible without the hub's deposit. Unlike existing payment networks, RouTEE provides high balance liquidity, and details about payments are concealed from hosts by leveraging trusted execution environments (TEEs). RouTEE is designed to make rational hosts behave honestly, by introducing a new routing fee scheme and a secure settlement method. Moreover, users do not need to monitor the blockchain in real-time or run full nodes. They can participate in RouTEE by simply verifying block headers through light clients; furthermore, having only one channel with RouTEE is sufficient to interact with other users. Our implementation demonstrates that RouTEE is highly efficient and outperforms Lightning Network that is the state-of-the-art payment network.

Jae-Yun Kim, Jun-Mo Lee, Yeon-Jae Koo et al.

As ethereum blockchain has become popular, the number of users and transactions has skyrocketed, causing an explosive increase of its data size. As a result, ordinary clients using PCs or smartphones cannot easily bootstrap as a full node, but rely on other full nodes such as the miners to run or verify transactions. This may affect the security of ethereum, so light bootstrapping techniques such as fast sync has been proposed to download only parts of full data, yet the space overhead is still too high. One of the biggest space overhead that cannot easily be reduced is caused by saving the state of all accounts in the block's state trie. Fortunately, we found that more than 90% of accounts are inactive and old transactions are hard to be manipulated. Based on these observations, this paper propose a novel optimization technique called ethanos that can reduce bootstrapping cost by sweeping inactive accounts periodically and by not downloading old transactions. If an inactive account becomes active, ethanos restore its state by running a restoration transaction. Also, ethanos gives incentives for archive nodes to maintain the old transactions for possible re-verification. We implemented ethanos by instrumenting the go-ethereum (geth) client and evaluated with the real 113 million transactions from 14 million accounts between 7M-th and 8M-th blocks in ethereum. Our experimental result shows that ethanos can reduce the size of the account state by half, which, if combined with removing old transactions, may reduce the storage size for bootstrapping to around 1GB. This would be reasonable enough for ordinary clients to bootstrap on their personal devices.