• Journal of Hydrogen and New Energy
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• v.30 no.2
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• pp.155-162
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• 2019

The energy management technology for an independent fuel cell-battery hybrid system is developed for a household usage. To develop an efficient energy management technology, a simulation model is first developed. After the model is verified with experimental results, three energy management schemes are developed. Three control techniques are a fuzzy logic control (FLC), a state machine control (SMC), and a hybrid method of FLC and SMC. As the fuel cell-battery hybrid system is used for a house, battery state of charge (SOC) regulation is the most important factor for an energy management because SOC should be kept constant every day for continuous usage. Three management schemes are compared to see SOC, power split, and fuel cell power variations effects. Experimental results are also presented and the most favorable strategy is the state machine combined fuzzy control method.

• Korean Journal of Plant Resources
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• v.19 no.3
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• pp.401-404
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• 2006

In order to select the seeding machine for mechanizing cultivation of rapeseed in southern areas of Korea, three different seeding machines, ridge rotary, power tiller ridge rotary, tractor ridge rotary were used for sowing one of the high yielding rapeseed cv. Hybrid with five different seeding methods. Seeding of ridge rotary was reduced the seeding effort with 45% and yield components such as plant height, ear length, number of branches and pods, pod length and seed setting rate were higher. The seeding of ridge rotary also was showed highest seed yield. On the basis of time requirement for seeding, vegetative and yield parameters ridge rotary seeding machine was a suitable seeding machine for rapeseed cultivation at the southern area of Korea.

• IE interfaces
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• v.14 no.1
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• pp.20-29
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• 2001

This paper presents a concurrent design approach that deals with manufacturing cell formation and cellular layout in Cellular Manufacturing System. Manufacturing cell formation is to group machines into machine cells dedicated to manufacture of part families, and cellular layout problem determines layout of the manufacturing cells within shop and layout of the machines within a cell. In this paper, a concurrent approach for design of machine cell and cellular layout is developed considering manufacturing parameters such as alternative process plans, alternative machines, production volume and processing time of part, and cost per unit time of operation. A mathematical model which minimizes total cost consisting of machine installation cost, machine operating cost, and intercell and intracell movements cost of part is proposed. A hybrid method based on genetic algorithm is proposed to solve the manufacturing cell formation and cellular layout design problem concurrently. The performance of the hybrid method is examined on several problems.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1032-1035
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• 2003

The field of handwriting recognition has been researched for many years. A hybrid classifier has been proven to be able to increase the recognition rate compared with a single classifier. In this paper, we combine support vector machine (SVM) and hidden Markov model (HMM) for offline handwritten numeral recognition. To improve the performance, we extract features adapted for each classifier and propose the modified SVM decision structure. The experimental results show that the proposed method can achieve improved recognition rate for handwritten numeral recognition.

• Journal of KIISE
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• v.43 no.3
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• pp.362-369
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• 2016

This study is directed toward the design of a hybrid algorithm for syllable-based Korean POS tagging. Previous syllable-based works on Korean POS tagging have relied on a sequence labeling method and mostly used only a machine learning method. We present a new algorithm integrating a machine learning method and a pre-analyzed dictionary. We used a Sejong tagged corpus for training and evaluation. While the machine learning engine achieved eojeol precision of 0.964, the proposed hybrid engine achieved eojeol precision of 0.990. In a Quiz domain test, the machine learning engine and the proposed hybrid engine obtained 0.961 and 0.972, respectively. This result indicates our method to be effective for Korean POS tagging.

• Convergence Security Journal
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• v.12 no.5
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• pp.71-77
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• 2012

Expenses in the form of personnel expenses in the past, in modern times, machine guards to gradually transition has been. This is because the machine guard is more efficient than personnel expenses. But due to false alarms, despite the high expectations of the effects of electronic security in the operation of the electronic security system due to factors that hinder the development of machine guards growth slows. Defect removal aspects of this paper, using IPA (Importance Performance Analysis) techniques to study the operation of electronic security systems and its importance in the development of machine guards, look at how high the technical aspects of electronic security systems composite type of malfunction to minimize crime sensor are presented.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.477-484
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• 2001

This paper proposes a hybrid learning algorithm combined momentum and kernel-adatron for improving the performance of regression support vector machine. The momentum is utilized for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution, and the kernel-adatron algorithm is also utilized for the capability by working in nonlinear feature spaces and the simple implementation. The proposed algorithm has been applied to the 1-dimension and 2-dimension nonlinear function regression problems. The simulation results show that the proposed algorithm has better the learning speed and performance of the regression, in comparison with those quadratic programming and kernel-adatron algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• Journal of the Korean Geosynthetics Society
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• v.22 no.1
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• pp.67-74
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• 2023

In order to monitor the stability of slopes, research on data-based slope failure prediction and early warning is increasing. However, most papers overlook the quality of data. Poor data quality can cause problems such as false alarms. Therefore, this paper proposes a two-step hybrid approach consisting of rules and machine learning models for quality control of data collected from slopes. The rule-based has the advantage of high accuracy and intuitive interpretation, and the machine learning model has the advantage of being able to derive patterns that cannot be explicitly expressed. The hybrid approach was able to take both of these advantages. Through a case study, the performance of using the two methods alone and the case of using the hybrid approach was compared, and the hybrid method was judged to have high performance. Therefore, it is judged that using a hybrid method is more appropriate than using the two methods alone for data quality control.