• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.91-98
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• 2010

Conventional path-finding has focused on finding short collision-free paths. However, as computer games become more sophisticated, it is required to take tactical information like ambush points or lines of enemy sight into account. One way to make this information have an effect on path-finding is to represent a heuristic function of a search algorithm as a weighted sum of tactics. In this paper we consider the problem of learning heuristic to optimize path-finding based on given tactical information. What is meant by learning is to produce a good weight vector for a heuristic function. Training examples for learning are given by a game level-designer and will be compared with search results in every search level to update weights. This paper proposes a learning algorithm integrated with search for tactical path-finding. The perceptron-like method for updating weights is described and a simulation tool for implementing these is presented. A level-designer can mark desired paths according to characters' properties in the heuristic learning tool and then it uses them as training examples to learn weights and shows traces of paths changing along with weight learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.73-78
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• 2023

Diabetes is chronic disease that is rapidly increasing in prevalence around the world, and mortality from complications continues to rise. This has made blood glucose management a critical challenge for modern society. The main methods used to manage blood glucose are diet, exercise, and medication. Among these, diet is one of the fundamental foundations of blood glucose management, avoiding foods that cause high blood glucose and minimizing blood glucose fluctuations, and is more accessible to people with diabetes as well as the general population. Currently, several platforms, both domestic and international, offer meal planning services, but this is mainly done by users or professional coaches. Accordingly, this paper implements an accurate Kcal calculation model based on DNN and presents a series of dietary algorithms for blood glucose management based on this.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.183-191
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• 2023

Cryptographic algorithms require extensive computational resources and rely on complex mathematical principles for security. However, IoT devices have limited resources, leading to insufficient computing power. As a result, lightweight cryptography has emerged, which uses fewer computational resources. NIST organized a competition to standardize lightweight cryptography and TinyJAMBU, one of the algorithms in the competition, is a permutation-based algorithm that repeats many permutation operations. In this paper, we implement TinyJAMBU on an 8-bit AVR processor with a proposedtechnique that includes a reverse shift method and precomputing some operations in a fixed key and nonce environment. Our techniques showed a maximum performance improvement of 7.03 times in permutation operations and 5.87 times in the TinyJAMBU algorithm, improving up to 9.19 times in a fixed key and nonce environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1713-1719
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• 2022

As the noise canceler with a small processing delay increases the sampling frequency, a better-quality output can be obtained. For a single buffer, processing delay occurs because it is impossible to write new data while the processor is processing the data. When synthesizing with anti-noise and output signal, this processing delay creates additional buffering overhead to match the phase. In this paper, we propose an accelerator structure that minimizes processing delay and increases processing speed by alternately performing read and write operations using the Symmetric Even-Odd-buffer. In addition, we compare the structural differences between the two methods of noise cancellation (Fast Fourier Transform noise cancellation and adaptive Least Mean Square algorithm). As a result, using an Symmetric Even-Odd-buffer the processing delay was reduced by 29.2% compared to a single buffer. The proposed Symmetric Even-Odd-buffer structure has the advantage that it can be applied to various canceling algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.383-389
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• 2023

The development of quantum computers and the emergence of quantum algorithms such as Shor's algorithm and Grover's algorithm pose a significant threat to the security of existing cipher systems. Quantum algorithms can efficiently perform mathematical operations that take a long time on traditional computers. This characteristic can significantly reduce the time it takes to break modern cipher systems that rely on mathematical problems. To prepare for quantum attacks based on these algorithms, existing ciphers must be implemented as quantum circuits. Many ciphers have already been implemented as quantum circuits, analyzing quantum resources required for attacks and verifying the quantum strength of the cipher. In this paper, we present quantum circuits for LED lightweight block ciphers and explain each function of quantum circuits. Thereafter, the resources for the LED quantum circuit are estimated and evaluated by comparing them with other lightweight block ciphers.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.303-314
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• 2023

This study aimed to assess the practical feasibility of advanced deformable image registration (DIR) algorithms in radiotherapy by employing two distinct datasets. The first dataset included 14 4D lung CT scans and 31 head and neck CT scans. In the 4D lung CT dataset, we employed the DIR algorithm to register organs at risk and tumors based on respiratory phases. The second dataset comprised pre-, mid-, and post-treatment CT images of the head and neck region, along with organ at risk and tumor delineations. These images underwent registration using the DIR algorithm, and Dice similarity coefficients (DSCs) were compared. In the 4D lung CT dataset, registration accuracy was evaluated for the spinal cord, lung, lung nodules, esophagus, and tumors. The average DSCs for the non-learning-based SyN and NiftyReg algorithms were 0.92±0.07 and 0.88±0.09, respectively. Deep learning methods, namely Voxelmorph, Cyclemorph, and Transmorph, achieved average DSCs of 0.90±0.07, 0.91±0.04, and 0.89±0.05, respectively. For the head and neck CT dataset, the average DSCs for SyN and NiftyReg were 0.82±0.04 and 0.79±0.05, respectively, while Voxelmorph, Cyclemorph, and Transmorph showed average DSCs of 0.80±0.08, 0.78±0.11, and 0.78±0.09, respectively. Additionally, the deep learning DIR algorithms demonstrated faster transformation times compared to other models, including commercial and conventional mathematical algorithms (Voxelmorph: 0.36 sec/images, Cyclemorph: 0.3 sec/images, Transmorph: 5.1 sec/images, SyN: 140 sec/images, NiftyReg: 40.2 sec/images). In conclusion, this study highlights the varying clinical applicability of deep learning-based DIR methods in different anatomical regions. While challenges were encountered in head and neck CT registrations, 4D lung CT registrations exhibited favorable results, indicating the potential for clinical implementation. Further research and development in DIR algorithms tailored to specific anatomical regions are warranted to improve the overall clinical utility of these methods.

• Computers and Concrete
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• v.33 no.1
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1042-1058
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• 2024

Terahertz (THz) communication is becoming a key technology for future 6G wireless networks because of its ultra-wide band. However, the implementation of THz communication systems confronts formidable challenges, notably beam splitting effects and high computational complexity associated with them. Our primary objective is to design a hybrid precoder that minimizes the Euclidean distance from the fully digital precoder. The analog precoding part adopts the delay-phase alternating minimization (DP-AltMin) algorithm, which divides the analog precoder into phase shifters and time delayers. This effectively addresses the beam splitting effects within THz communication by incorporating time delays. The traditional digital precoding solution, however, needs matrix inversion in THz massive multiple-input multiple-output (MIMO) communication systems, resulting in significant computational complexity and complicating the design of the analog precoder. To address this issue, we exploit the characteristics of THz massive MIMO communication systems and construct the digital precoder as a product of scale factors and semi-unitary matrices. We utilize Schatten norm and Hölder's inequality to create semi-unitary matrices after initializing the scale factors depending on the power allocation. Finally, the analog precoder and digital precoder are alternately optimized to obtain the ultimate hybrid precoding scheme. Extensive numerical simulations have demonstrated that our proposed algorithm outperforms existing methods in mitigating the beam splitting issue, improving system performance, and exhibiting lower complexity. Furthermore, our approach exhibits a more favorable alignment with practical application requirements, underlying its practicality and efficiency.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.93-99
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• 2024

In this paper, we derive the Partial Differential Bond Price Equation (PDBPE) by using Ito's Lemma to determine the pricing of bond on term-structure of interest rate (TSIR) model with jump. From PDBPE, the Maclaurin series (MS) and the moment-generating function (MGF) for the exponential function are used to obtain a numerical solution (NS) of the bond prices. And an algorithm for determining bond prices using Monte Carlo Simulation (MCS) techniques is proposed, and the pricing of bond is determined through the simulation process. Comparing the results of the implementation of the above two pricing methods, the relative error (RE) is obtained, which means the ratio of NS and MCS. From the results, we can confirm that the RE is less than around 2.2%, which means that the pricing of bond can be predicted very accurately using the proposed algorithms as well as numerical analysis. Moreover, it was confirmed that the bond price obtained using the MS has a relatively smaller error than the pricing of bond obtained by using the MGF.

• Smart Structures and Systems
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• v.33 no.5
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• pp.365-374
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• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.