• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.339-346
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• 2005

Artificial potential field (APF) is a widely used method for local path planning of autonomous mobile robot. So far, many different types of APF have been implemented. Once the artificial potential functions are selected, how to choose appropriate parameters of the functions is also an important work. In this paper, a detailed analysis is given on how to choose proper parameters of artificial functions to eliminate free path local minima and avoid collision between robots and obstacles. Two kinds of potential functions: Gaussian type and Quadratic type of potential functions are used to solve the above local minima problem respectively. To avoid local minima occurred in realistic situations such as 1) a case that the potential of the goal is affected excessively by potential of the obstacle, 2) a case that the potential of the obstacle is affected excessively by potential of the goal, the design guidelines for selecting appropriate parameters of potential functions are proposed.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.489-496
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• 2010

In this paper, a self-organization framework for swarm systems in three dimensions is presented. The framework uses artificial potential functions(APFs) to direct the robots toward the goal as well as to keep them in a swarm system. This research extends conventional APFs used for self-organizations in two dimension environment to three dimensions. In three dimension environment, the ground potential for the boundary surfaces that commonly appear in three dimension environments is proposed. Accordingly, the comparison between the paths without and with the ground potentials shows the necessity and effect of ground potentials. Extensive simulations are given to show the effectiveness of the extended potentials and various properties in three dimension environments.

• Journal of KIISE
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• v.42 no.10
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• pp.1231-1238
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• 2015

Traffic light rules are one among the most common and important safety rules as the directly correlate with the safety of pedestrians. Consequently, an algorithm is required to cause an automated (or semi-automated) vehicle to observe traffic light signals. We present a novel, artificial potential function to guide an automated vehicle through traffic lights. Our function consists of three potential function components representing the three traffic light colors: green, yellow, and red. The traffic light potential function smoothly changes an artificial potential field using the elapsed time for the current light and light conversion. Our traffic light potential function is combined with other potential functions to guide vehicles' movement and constructs the final artificial potential field. Using various simulations, we found or method successfully guided the vehicle to observe traffic lights while behaving like human-controlled cars.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.5
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• pp.448-454
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• 2008

This paper deals with a behavior based decentralized control strategy for UAV swarming utilizing the artificial potential functions and the sliding mode control technique. Individual interactions for swarming behavior are modeled using the artificial potential functions. The motion of individual UAV is directed toward the negative gradient of the combined potential. For tracking the reference trajectory of UAV swarming, a swarming center is considered as the object of control. The sliding-mode control technique is adopted to make the proposed swarm control strategy robust with respect to the system uncertainties and the varying mission environment. Numerical simulation is performed to verify the performance of the proposed controller.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.47-54
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• 2023

This article reviews recent advances and perspectives in neuroscience related to consciousness, cognition, and neural networks in the brain. The neural mechanisms underlying cognitive processes, such as perception, attention, memory, and decision-making, are explored. The article also examines how these processes give rise to our experience of consciousness. The implications of these findings for our understanding of the brain and its functions are presented, as well as potential applications of this knowledge in fields such as medicine, psychology, and artificial intelligence. Additionally, the article explores the concept of a quantum viewpoint concerning consciousness, cognition, and creativity and how incorporating DNA as a key element could reconcile classical and quantum perspectives on human behaviour, consciousness, and cognition, as explained by genomic psychological theory. Furthermore, the article explains how the human brain processes external stimuli through the sensory nervous system and how it can be simulated using an artificial neural network (ANN) consisting of one input layer, multiple hidden layers, and an output layer. The law of learning is also discussed, explaining how ANNs work and how the modification of weight values affects the output and input values. The article concludes with a discussion of future research directions in this field, highlighting the potential for further discoveries and advancements in our understanding of the brain and its functions.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.448-454
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• 2018

Artificial bee colony (ABC) algorithm has attracted significant interests recently for solving the multivariate optimization problem. However, it still faces insufficiency of slow convergence speed and poor local search ability. Therefore, in this paper, a modified ABC algorithm with bees' number reallocation and new search equation is proposed to tackle this drawback. In particular, to enhance solution accuracy, more bees in the population are assigned to execute local searches around food sources. Moreover, elite vectors are adopted to guide the bees, with which the algorithm could converge to the potential global optimal position rapidly. A series of classical benchmark functions for frequency-modulated sound waves are adopted to validate the performance of the modified ABC algorithm. Experimental results are provided to show the significant performance improvement of our proposed algorithm over the traditional version.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.355-362
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• 2009

In this paper, we present a framework for managing group behaviors in multi-agent swarm systems. The framework explores the benefits by dynamic associations with the proposed artificial potential functions to realize complex swarming behaviors. A key development is the introduction of a set of flocking by dynamic association (DA) algorithms that effectively deal with a host of swarming issues such as cooperation for fast migration to a target, flexible and agile formation, and inter-agent collision avoidance. In particular, the DA algorithms employ a so-called systematic singular association (SSA) rule for fast migration to a target and compact formation through inter-agent interaction. The resulting algorithms enjoy two important interrelated benefits. First, the SSA rule greatly reduces time-consuming for migration and satisfies low possibility that agents may be lost. Secondly, the SSA is advantageous for practical implementations, since it considers for agents even the case that a target is blocked by obstacles. Extensive simulation presents to illustrate the viability and effectiveness of the proposed framework.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• The Plant Pathology Journal
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• v.40 no.1
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• pp.83-97
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• 2024

Fusarium graminearum, the causal agent of Fusarium head blight (FHB) in cereal crops, employs the production of sexual fruiting bodies (perithecia) on plant debris as a strategy for overwintering and dissemination. In an artificial condition (e.g., carrot agar medium), the F. graminearum Z3643 strain was capable of producing perithecia predominantly in the central region of the fungal culture where aerial hyphae naturally collapsed. To unravel the intricate relationship between natural aerial hyphae collapse and sexual development in this fungus, we focused on 699 genes differentially expressed during aerial hyphae collapse, with 26 selected for further analysis. Targeted gene deletion and quantitative real-time PCR analyses elucidated the functions of specific genes during natural aerial hyphae collapse and perithecium formation. Furthermore, comparative gene expression analyses between natural collapse and artificial removal conditions reveal distinct temporal profiles, with the latter inducing a more rapid and pronounced response, particularly in MAT gene expression. Notably, FGSG_09210 and FGSG_09896 play crucial roles in sexual development and aerial hyphae growth, respectively. Taken together, it is plausible that if aerial hyphae collapse occurs on plant debris, it may serve as a physical cue for inducing perithecium formation in crop fields, representing a survival strategy for F. graminearum during winter. Insights into the molecular mechanisms underlying aerial hyphae collapse provides offer potential strategies for disease control against FHB caused by F. graminearum.