• Journal of Nutrition and Health
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• v.12 no.1
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• pp.51-58
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• 1979

It is a general trend everywhere that the uses of vegetable oils are increasing due to the fact that they are effective in curing and preventing symptoms of high blood pressure and various heart failure conditions. At the same time the concept that oxidative rancidity is caused by the oxidation of unsaturated fatty acid moieties whose subsequent decomposition gives rise to various undesirable, sometimes toxic compounds is now well accepted. Linolenic acid (C, 18:3) is one of highly unsaturated and readily oxidizable fatty acid. The content of this essential polyunsaturated fatty acid in perilla seed oil (PSO) was found to be as high as 48% while only 1.5% in sesame seed oil (SSO). In this experiment the oxidative stability of PSO was compared with that of SSO. The experimental test group were as follows: A) Stored at different temperatures, namely $4^{\circ}C,\;30^{\circ}C,$ and $60^{\circ}C,$ B) Stored at room temperature $(20{\pm}5^{\circ}C)$ ; a. protected from sunlight and air, b. exposed to air without sunlight c. exposed to sunlight but protected front air, d. completely exposed to both air and sunlight. The following results were obtained; 1) It was found to be most stable against oxidation to store both PSO and SSO under the low temperature $(4^{\circ}C)$ condition. According to P.V. measurements it was found to be safe to keep both oils up to $30^{\circ}C$ for at least 8 weeks. When exposed to air, sunlight and high temperature $(60^{\circ}C)$, P.V. of PSO reached there peak values, which were much higher than those of SSO. This explains much of its instability as compared to SSO against oxidation. 2) The effect of high temperature $(60^{\circ}C)$ on A.V. was found to be more striking than those of all the other storage conditions. The condition of refrigeration was most effective in keeping A.V. low for both oils as was the case in P.V. 3) For both oils, I.V. decreased throughout the experimental period (8 weeks). The range of decrement was larger for PSO than SSO. 4) There was no significant change in the compositions of fatty acids of SSO caused by various experimental storage conditions. But for PSO the compositions of stearic, oleic and linoleic acid were decreased, whereas linolenic acid was increased proportionally.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2070-2086
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• 2014

As a kind of personal lifelog data, activity data have been considered as one of the most compelling information to understand the user's habits and to calibrate diagnoses. In this paper, we proposed a robust algorithm to sampling rates for human activity recognition, which identifies a user's activity using accelerations from a triaxial accelerometer in a smartphone. Although a high sampling rate is required for high accuracy, it is not desirable for actual smartphone usage, battery consumption, or storage occupancy. Activity recognitions with well-known algorithms, including MLP, C4.5, or SVM, suffer from a loss of accuracy when a sampling rate of accelerometers decreases. Thus, we start from particle swarm optimization (PSO), which has relatively better tolerance to declines in sampling rates, and we propose PSO with an adaptive boundary correction (ABC) approach. PSO with ABC is tolerant of various sampling rate in that it identifies all data by adjusting the classification boundaries of each activity. The experimental results show that PSO with ABC has better tolerance to changes of sampling rates of an accelerometer than PSO without ABC and other methods. In particular, PSO with ABC is 6%, 25%, and 35% better than PSO without ABC for sitting, standing, and walking, respectively, at a sampling period of 32 seconds. PSO with ABC is the only algorithm that guarantees at least 80% accuracy for every activity at a sampling period of smaller than or equal to 8 seconds.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.490-498
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• 2016

Proportional Integral Derivative (PID) controller tuning is an area of interest for researchers in many areas of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of bacteria foraging and particle swarm optimization. BFO algorithm has recently emerged as a very powerful technique for real parameter optimization. To overcome delay in an optimization, combine the features of BFOA and PSO for tuning the PID controller. This new algorithm is proposed to combine both the algorithms to get better optimization values. The real time prototype model of paper machine is designed and controlled by using PIC microcontroller embedded with the programming in C language.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.245-254
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• 2012

In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.606-609
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• 2015

본 연구에서는 support vector regression (SVR) 및 매개변수 최적화 알고리즘을 이용한 하천수위 예측모델을 구축하고 이를 실제 유역에 적용하여 모델 효율성을 평가하였다. 여기서, SVR은 하천수위를 예측하기 위한 예측모델로서 채택되었으며, 커널함수 (Kernel function)로서는 radial basis function (RBF)을 선택하였다. 최적화 알고리즘은 SVR의 최적 매개변수 (C?, cost parameter or regularization parameter; ${\gamma}$, RBF parameter; ${\epsilon}$, insensitive loss function parameter)를 탐색하기 위하여 적용되었다. 매개변수 최적화 알고리즘으로는 grid search (GS), genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) 알고리즘을 채택하였으며, 비교분석을 통해 최적화 알고리즘의 적용성을 평가하였다. 또한 SVR과 최적화 알고리즘을 결합한 모델 (SVR-GS, SVR-GA, SVR-PSO, SVR-ABC)은 기존에 수자원 분야에서 널리 적용되어온 신경망(Artificial neural network, ANN) 및 뉴로퍼지 (Adaptive neuro-fuzzy inference system, ANFIS) 모델과 비교하였다. 그 결과, 모델 효율성 측면에서 SVR-GS, SVR-GA, SVR-PSO 및 SVR-ABC는 ANN보다 우수한 결과를 나타내었으며, ANFIS와는 비슷한 결과를 나타내었다. 또한 SVR-GA, SVR-PSO 및 SVR-ABC는 SVR-GS보다 상대적으로 우수한 결과를 나타내었으며, 모델 효율성 측면에서 SVR-PSO 및 SVR-ABC는 가장 우수한 모델 성능을 나타내었다. 따라서 본 연구에서 적용한 매개변수 최적화 알고리즘은 SVR의 매개변수를 최적화하는데 효과적임을 확인할 수 있었다. SVR과 최적화 알고리즘을 이용한 하천수위 예측모델은 기존의 ANN 및 ANFIS 모델과 더불어 하천수위 예측을 위한 효과적인 도구로 사용될 수 있을 것으로 판단된다.

• Journal of Power Electronics
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• v.15 no.3
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• pp.775-785
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• 2015

Many equalization circuits have been proposed to improve pack performance and reduce imbalance. Although bidirectional equalization topologies are promising in these methods, pre-equalization global equalization strategy is lacking. This study proposes a novel state-of-charge (SoC) equalization algorithm for bidirectional equalizer based on particle swarm optimization (PSO), which is employed to find optimal equalization time and steps. The working principle of bidirectional equalization topologies is analyzed, and the reason behind the application of SoC as a balancing criterion is explained. To verify the performance of the proposed algorithm, a pack with 12 LiFePO4 batteries is applied in the experiment. Results show that the maximum SoC gap is within 2% after equalization, and the available pack capacity is enhanced by 13.2%. Furthermore, a comparison between previously used methods and the proposed PSO equalization algorithm is presented. Experimental tests are performed, and results show that the proposed PSO equalization algorithm requires fewer steps and is superior to traditional methods in terms of equalization time, energy loss, and balancing performance.

• Journal of Korean Neurosurgical Society
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• v.47 no.2
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• pp.95-101
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• 2010

Objective : Clinical, radiographic, and outcomes assessments, focusing on complications, were performed in patients who underwent pedicle subtraction osteotomy (PSO) to assess correction effectiveness, fusion stability, procedural safety, neurological outcomes, complication rates, and overall patient outcomes. Methods : We analyzed data obtained from 13 consecutive PSO-treated patients presenting with fixed sagittal imbalances from 1999 to 2006. A single spine surgeon performed all operations. The median follow-up period was 73 months (range 41-114 months). Events during peri operative course and complications were closely monitored and carefully reviewed. Radiographs were obtained and measurements were done before surgery, immediately after surgery, and at the most recent follow-up examinations. Clinical outcomes were assessed using the Oswestry Disability Index and subjective satisfaction evaluation. Results : Following surgery, lumbar lordosis increased from $-14.1^{\circ}{\pm}20.5^{\circ}$ to $-46.3^{\circ}{\pm}12.8^{\circ}$ (p<0.0001). and the C7 plumb line improved from $115{\pm}43\;mm$ to $32{\pm}38\;mm$ (p<0.0001). There were 16 surgery-related complications in 8 patients; 3 intraoperative, 3 perioperative, and 10 late-onset postoperative. The prevalence of proximal junctional kyphosis (PJK) was 23% (3 of 13 patients). However, clinical outcomes were not adversely affected by PJK. Intraoperative blood loss averaged 2,984 mL. The C7 plumb line values and postoperative complications were closely correlated with clinical results. Conclusion : Intraoperative or postoperative complications are relatively common following PSO. Most late-onset complications in PSO patients were related to PJK and instrumentation failure. Correcting the C7 plumb line value with minimal operative complications seemed to lead to better clinical results.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1873_1874
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• 2009

본 논문에서는 대표적인 시스템 모델링 도구중의 하나인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)를 설계한다. 제안된 RBF 뉴럴 네트워크는 은닉층의 활성함수로서 Fuzzy C-Means 클러스터링을 사용하며 더 나아가 모델의 최적화를 위해 PSO 알고리즘을 사용하여 은닉층의 노드 수와 다수의 입력을 가질 경우 입력의 종류를 동정한다. 제안한 모델의 성능을 평가하기 위해 NOx 데이터를 적용하였으며 제안된 모델의 근사화와 일반화 능력을 분석한다.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1329-1339
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• 2017

This paper presents the power scheduling of a multi-microgrid (MMG) system using an optimization technique called particle swarm optimization (PSO). The PSO technique has been shown to be most effective at solving the various problems of the economic dispatch (ED) in a power system. In addition, a new MMG system configuration is proposed in this paper, through which the optimal power flow is achieved. Both optimization and power trading methods within an MMG are studied. The results of implementing PSO in an MMG system for optimal power flow and cost minimization are obtained and compared with another attractive and efficient optimization technique called the genetic algorithm (GA). The comparison between these two effective methods provides very competitive results, and their operating costs also appear to be comparable. Finally, in this study, power scheduling and a power trading method are obtained using the MATLAB program.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.65-73
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• 2015

This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.