• Journal of information and communication convergence engineering
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• v.4 no.1
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• pp.23-27
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• 2006

The discipline of software performance is very broad; it influences all aspects of the software development lifecycle, including architecture, design, deployment, integration, management, evolution and servicing. Thus, the complexity of software is an important aspect of development and maintenance activities. Much research has been dedicated to defining different software measures that capture what software complexity is. In most cases, the description of complexity is given to humans in forms of numbers. These quantitative measures reflect human-seen complexity with different levels of success. Software complexity growth has been recognized to be beyond human control. In this paper, we have focused our discussion on the increasing software complexity and the issue with the problems being faced in managing this complexity. This increasing complexity in turn affects the software productivity, which is declining with increase in its complexity.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.194-204
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• 2012

Measuring task complexity of movement skill is an important factor to evaluate a difficulty of learning and/or imitating a task for autonomous robots. Although many complexity-measures are proposed in research areas such as neuroscience, physics, computer science, and biology, there have been little attention on the robotic tasks. To cope with measuring complexity of robotic task, we propose an information-theoretic measure for task complexity of movement skills. By modeling proprioceptive as well as exteroceptive sensor data as multivariate Gaussian distribution, movements of a task can be modeled as probabilistic model. Additionally, complexity of temporal variations is modeled by sampling in time and modeling as individual random variables. To evaluate our proposed complexity measure, several experiments are performed on the real robotic movement tasks.

• Journal of Engineering Education Research
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• v.19 no.2
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• pp.60-69
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• 2016

This paper deals with the selection method of effective engineering design problem based on LEGO Mindstorm NXT for basic design education. By YouTube case study of various LEGO-based engineering designs for olympic sports, performance criteria have been developed including programming complexity, structural complexity, sensor/actuator complexity and variety of game operation. Programming complexity includes range of programming code length and possible program variety. Structural complexity includes variety of structural elements such as length, shape, weight, and volume to overcome design restrictions. Sensor/actuator complexity includes variety of sensor used and number of possible actuator assemblies. Variety of game operation includes game complexity and required creativity to make LEGO robots. Based on these performance criteria, four representative sports were selected as the candidates for effective engineering design problem. Finally, feasibility and attributes of each candidate were verified by real implementation examples.

• Journal of the Korean Mathematical Society
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• v.45 no.1
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• pp.221-228
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• 2008

In this paper, we study the complexity of semigroup actions using complexity functions of open covers. The main results are as follows: (1) A dynamical system is equicontinuous if and only if any open cover has bounded complexity; (2) Weak-mixing implies scattering; (3) We get a criterion for the scattering property.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.20 no.2
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• pp.107-121
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• 2016

We analyze the complexity of the LLL algorithm, invented by Lenstra, Lenstra, and $Lov{\acute{a}}sz$ as a a well-known lattice reduction (LR) algorithm which is previously known as having the complexity of $O(N^4{\log}B)$ multiplications (or, $O(N^5({\log}B)^2)$ bit operations) for a lattice basis matrix $H({\in}{\mathbb{R}}^{M{\times}N})$ where B is the maximum value among the squared norm of columns of H. This implies that the complexity of the lattice reduction algorithm depends only on the matrix size and the lattice basis norm. However, the matrix structures (i.e., the correlation among the columns) of a given lattice matrix, which is usually measured by its condition number or determinant, can affect the computational complexity of the LR algorithm. In this paper, to see how the matrix structures can affect the LLL algorithm's complexity, we derive a more tight upper bound on the complexity of LLL algorithm in terms of the condition number and determinant of a given lattice matrix. We also analyze the complexities of the LLL updating/downdating schemes using the proposed upper bound.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.5
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• pp.151-161
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• 2021

Traditional Korean medicine (TKM) takes a holistic view that emphasizes the balance between the elements constituting the human body or between the human body and the external environment. To investigate the holistic properties of TKM, here we propose to apply the methodology of complexity science to the TKM research. Complexity science is a discipline for studying complex systems with interactions between components that raise the behaviour as a whole which can be more than the sum of their parts. We first provide an introduction to the complexity science and its research methods, particularly focusing on network science and data science approaches. Next, we briefly present the current status of TKM research employing these methods. Finally, we provide suggestions for future research elucidating the underlying mechanism of TKM, both in terms of biomedicine and humanities.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.508-521
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• 2020

We compared a novel encoding Lempel-Ziv complexity (ELZC) with three common complexity algorithms i.e., approximate entropy (ApEn), sample entropy (SampEn), and classic Lempel-Ziv complexity (CLZC) so as to determine a satisfied complexity and its corresponding quality indices for assessing quality of multi-lead electrocardiogram (ECG). First, we calculated the aforementioned algorithms on six artificial time series in order to compare their performance in terms of discerning randomness and the inherent irregularity within time series. Then, for analyzing sensitivity of the algorithms to content level of different noises within the ECG, we investigated their change trend in five artificial synthetic noisy ECGs containing different noises at several signal noise ratios. Finally, three quality indices based on the ELZC of the multi-lead ECG were proposed to assess the quality of 862 real 12-lead ECGs from the MIT databases. The results showed the ELZC could discern randomness and the inherent irregularity within six artificial time series, and also reflect content level of different noises within five artificial synthetic ECGs. The results indicated the AUCs of three quality indices of the ELZC had statistical significance (>0.500). The ELZC and its corresponding three indices were more suitable for multi-lead ECG quality assessment than the other three algorithms.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.408-427
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• 2011

Intensive computational complexity is an obstacle of enabling multiview video coding for real-time applications. In this paper, we present a fast macroblock (MB) mode selection algorithm for B frames which are based on the computational complexity analyses between the MB mode selection and reference frame selection. Three strategies are proposed to reduce the coding complexity jointly. First, the temporal correlation of MB modes between current MB and its temporal corresponding MBs is utilized to reduce computational complexity in determining the optimal MB mode. Secondly, Lagrangian cost of SKIP mode is compared with that of Inter $16{\times}16$ modes to early terminate the mode selection process. Thirdly, reference frame correlation among different Inter modes is exploited to reduce the number of reference frames. Experimental results show that the proposed algorithm can promote the encoding speed by 3.71~7.22 times with 0.08dB PSNR degradation and 2.03% bitrate increase on average compared with the joint multiview video model.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3682-3694
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• 2022

The expansion of a country's ecological footprint generates resources for economic development. China's import bill and carbon footprint can be reduced by investing in green transportation and energy technologies. A sustainable environment depends on the cessation of climate change; the current study investigates nuclear energy efficiency, economic complexity, air transportation, and industrial improvement for reducing environmental footprint. Using data spanning the years 1983-2016, the dynamic autoregressive distributed lag simulation method has demonstrated the short- and long-term variability in the impact of regressors on the ecological footprint. The study findings revealed that economic complexity in China had been found to have a statistically significant impact on the country's ecological footprint. Moreover, the industrial improvement process is helpful for the ecological footprint in China. In the short term, air travel has a negative impact on the ecological footprint, but this effect diminishes over time. Additionally, energy innovation is negative and substantial both in the short and long run, thus demonstrating its positive role in reducing the ecological footprint. Policy implications can be extracted from a wide range of issues, including economic complexity, industrial improvement, air transportation, energy innovation, and ecological impact to achieve sustainable goals.