• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.3105-3107
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• 1999

In this paper, we propose a new approach to autonomous wall-following of wheeled mobile robots using hybrid control system. The hybrid control approach IS introduced to the motion control of nonholonomic mobile robots in the Indoor navigation problems. In hybrid control architecture, the discrete states are defined by the user-defined constraints, and the reference motion commands are specified In the abstracted motions. The hybrid control system applied to motion planning and autonomous navigation with obstacle avoidance In indoor navigation problem. Simulation results show that it is an effective method for the autonomous navigation in indoor environments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.152-152
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• 2000

In this paper, we propose a new approach to intelligent motion and autonomous maneuvering of mobile robots using hybrid system. In high Level, the discrete states are defined by using the sensor-based search windows and the reference motions of a mobile robot as a low vevel are specified in the abstracted motions, The mobile robots can perform both the motion planning and autonomous maneuvering with obstacle avoidance in indoor navigation problem. Simulation and experimental results show that hybrid system approach is an effective method for the autonomous maneuvering in indoor environments.

• 한국산업정보학회논문지
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• 제20권6호
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• pp.57-65
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• 2015

제조최적화 문제는 비선형 형태의 설계변수로 표시되며, 다양하고 복잡한 제약들을 만족하는 조건하에서 최적해를 구하는 문제이다. 이러한 제조최적화 문제 해결을 위하여 본 연구에서는 혼합형접근법을 제안한다. 제안된 혼합형접근법은 기존의 유전알고리즘(Genetic algorithm: GA)과 쿠쿠탐색(Cuckoo search: CS) 및 언덕오르기법(Hill climbing method: HCM)을 혼합한 형태로 구성된다. 제안된 혼합형접근법에서 GA는 전역적탐색(Global search)를 위해 사용되고, CS는 GA탐색과정에서 발생하는 단점을 개선하기 위해 적용되고, 마지막으로 HCM은 GA와 CS 탐색 이후의 수렴된 지역을 정밀하게 탐색하기 위한 지역적탐색(Local search)을 위해 적용된다. 실험분석에서는 다양한 형태의 제조최적화 문제가 제시되어 본 연구에서 제안된 혼합형접근법와 기존접근법들의 수행도를 각각 비교, 분석하였으며, 그 결과는 본 연구에서 제안한 혼합형접근법의 수행도가 기존접근법들의 수행도보다 더 우수한 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4408-4428
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• 2019

Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.11-25
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• 2024

The proliferation of IoT devices has presented an unprecedented challenge in managing device identities securely and efficiently. In this paper, we introduce an innovative Hybrid Blockchain-Based Approach for IoT Identity Management that prioritizes both security and efficiency. Our hybrid solution, strategically combines the advantages of direct and indirect connections, yielding exceptional performance. This approach delivers reduced latency, optimized network utilization, and energy efficiency by leveraging local cluster interactions for routine tasks while resorting to indirect blockchain connections for critical processes. This paper presents a comprehensive solution to the complex challenges associated with IoT identity management. Our Hybrid Blockchain-Based Approach sets a new benchmark for secure and efficient identity management within IoT ecosystems, arising from the synergy between direct and indirect connections. This serves as a foundational framework for future endeavors, including optimization strategies, scalability enhancements, and the integration of advanced encryption methodologies. In conclusion, this paper underscores the importance of tailored strategies in shaping the future of IoT identity management through innovative blockchain integration.

• Smart Structures and Systems
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• 제14권6호
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• pp.1197-1220
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• 2014

Accurate actuator tracking is critical to achieve reliable real-time hybrid simulation results for earthquake engineering research. The frequency-domain evaluation approach provides an innovative way for more quantitative post-simulation evaluation of actuator tracking errors compared with existing time domain based techniques. Utilizing the Fast Fourier Transform the approach analyzes the actuator error in terms of amplitude and phrase errors. Existing application of the approach requires using the complete length of the experimental data. To improve the computational efficiency, two techniques including data decimation and frequency decimation are analyzed to reduce the amount of data involved in the frequency-domain evaluation. The presented study aims to enhance the computational efficiency of the approach in order to utilize it for future on-line actuator tracking evaluation. Both computational simulation and laboratory experimental results are analyzed and recommendations on the two decimation factors are provided based on the findings from this study.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2003년도 추계학술대회 및 정기총회
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• pp.201-204
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• 2003

A hybrid artificial intelligence approach based on combining case based reasoning and neural networks is presented. The approach is designed to allow for solving blowing control of BOF(basic oxygen furnace), example of which lie at the core of steelmaking process control systems application in the steel industry. According to this hybrid approach, the system, when faced with a new problem, first retrieves similar cases and neural network is used to solve the problem. Experimental Results indicate that combining case based reasoning and neural network offers an efficient approach to solving control and prediction problem

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2000년도 춘계학술발표대회
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• pp.133-136
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• 2000

In recent years, hybrid neural network approaches which combine neural networks and mechanistic models have been gaining considerable interests. These approaches are potentially very efficient to obtain more accurate predictions of process dynamics by combining mechanistic and neural models in such a way that the neural network model properly captures unknown and nonlinear parts of the mechanistic model. In this work, such an approach was applied in the modeling of a full-scale coke wastewater treatment process. First, a simplified mechanistic model was developed based on the Activated Sludge Model No.1 and the specific process knowledge, Then neural network was incorporated with the mechanistic model to compensate the errors between the mechanistic model and the process data. Simulation and actual process data showed that the hybrid modeling approach could predict accurate process dynamics of industrial wastewater treatment plant. The promising results indicated that the hybrid modeling approach could be a useful tool for accurate and cost-effective modeling of biochemical processes.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.215-235
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• 2017

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권1호
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• pp.71-77
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• 2016

Purpose: Root cause analysis (RCA) refers to any systematic process that identifies the causes that contribute to a focus event. The immediate cause of a focus event is often a symptom of underlying causes and may not truly identify the root causes that should be identified and addressed. Currently many RCA tools are available. Different investigators use different RCA tools on different issues. No standardized or commonly agreed way to analyse root causes exists. The purpose of this study is to propose the methodology of RCA process commonly useable for various issues. Methods: The methodology of RCA process is produced based on the hybrid RCA tools. The effectiveness assessment matrix of actions through the root cause candidates is presented. Results: No single RCA technique proposed has so far covered all necessary aspects. A hybrid approach which combines the best features of various techniques is proposed. The effectiveness assessment matrix helps us to identify the root cause to correct or eliminate system vulnerabilities effectively. Conclusion: This hybrid approach and effectiveness assessment matrix can provide guidance of RCA process across many industries and situations.