• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2455-2458
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• 2005

In this paper, we analyzed the effect of a training system for improving equilibrium sense. This training system consists of an unstable platform, a force plate, a computer, and training programs. Using the system with training programs, we performed various experiments to train the equilibrium sense of fifteen subjects. To evaluate the effect of the training system, we measured the time a subject maintains a focus, the moving time to the target, and the absolute deviation of the trace. We analyzed these parameters obtained before and after the training using paired-samples T-test. The results showed that the subjects experienced a distinctive enhancement of their equilibrium senses through the training using our system.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.465-477
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• 2018

The purpose of this study is to analyze the effect of three - week high altitude ski training on the myocardial performance of cross - country skiers and to provide basic data for the future improvement of cross - country skiers'. The subjects were 6 cross - country skiing male college athletes. To investigate the effects of periodic and high altitude training on cross - country skiers, a general linear model ANOVA with repeated measure And analyzed using the Paired Samples t-test. In high altitude ski training for 3 weeks, the body composition did not change but the isokinetic muscular function of the shoulder joint, hip joint, knee joint, and ankle joint was decreased. Therefore, further study is needed if it is considered that continuous strength training should be performed during the ski training period such as SP period.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.499-506
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• 2020

The study examines the influence of individual characteristics, training content, and manager support on the effectiveness of on-the-job (OJT) training in the banking and finance industry. A simple random sampling technique was used to select the samples. Questionnaires were distributed to respondents in order to obtain the data. Using cross-sectional data obtained from 396 respondents in Bank A in Malaysia, the multiple regression results show that self-efficacy, motivation to learn, training content, and manager support have positive influence on OJT training effectiveness. Among all these factors, manager support is very highly correlated with OJT training effectiveness. The findings have given fruitful insight of the crucial roles of OJT training in the respective bank, particularly to bring forward the roles of systematic design and implementation of OJT training. This study is not only expanding knowledge in OJT and training, but offers managers practical insights in developing good OJT training program by considering employees need, capabilities, skills and job requirement. Furthermore, this study also provides a valuable framework in identifying the effectiveness of OJT training program for certain jobs. Further discussion of the research findings and its implications to theoretical knowledge of training and managers are promised at the end of the article.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.643-646
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• 2013

We present an improved AdaBoost algorithm to avoid overfitting phenomenon. AdaBoost is widely known as one of the best solutions for object detection. However, AdaBoost tends to be overfitting when a training dataset has noisy samples. To avoid the overfitting phenomenon of AdaBoost, the proposed method divides positive samples into K clusters using k-means algorithm, and then uses only one cluster to minimize the training error at each iteration of weak learning. Through this, excessive partitions of samples are prevented. Also, noisy samples are excluded for the training of weak learners so that the overfitting phenomenon is effectively reduced. In our experiment, the proposed method shows better classification and generalization ability than conventional boosting algorithms with various real world datasets.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.106-109
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• 1998

Wireless communication systems, in particular, must operate in a crowded electro-magnetic environmnet where in-band undesired signals are treated as noise by the receiver. These interfering signals are often random but not Gaussian Due to nongaussian noise, the distribution of the observables cannot be specified by a finite set of parameters; instead r-dimensioal sample space (pure noise samples) is equiprobably partitioned into a finite number of disjointed regions using quantiles and a vector quantizer based on training samples. If we assume that the detected symbols are correct, then we can observe the pure noise samples during the training and transmitting mode. The algorithm proposed is based on a piecewise approximation to a regression function based on quantities and conditional partition moments which are estimated by a RMSA (Robbins-Monro Stochastic Approximation) algorithm. In this paper, we develop a diversity combiner with modified detector, called Non-Linear Detector, and the receiver has a differential phase detector in each diversity branch and at the combiner each detector output is proportional to the second power of the envelope of branches. Monte-Carlo simulations were used as means of generating the system performance.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.49-63
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• 1997

We consider the problem of classifying a p x 1 observation into one of two multivariate normal populations when the training smaples contain a block of missing observation. A new classification procedure is proposed which is a linear combination of two discriminant functions, one based on the complete samples and the other on the incomplete samples. The new discriminant function is easy to use.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.21-28
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• 2015

Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale in each feature dimension and error rate in classification. Most of the existing normalization methods, however, do not use the class labels of data points and, as a result, do not guarantee the optimality of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature normalization to reduce classification error more. Training sample selection is a common technique for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• Journal of Korean Academy of Nursing Administration
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• v.8 no.4
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• pp.575-583
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• 2002

Purpose : The purpose of this study was examine the effects of Self-Development training on the Self-Identity for head nurses. Methods : The sample consisted of 24 head nurses in one university hospital in Seoul. The subjects were divided into two groups for the training. Self-Development training was developed by the researcher for during 4 sessions in two weeks that is, 2 hours a day/ 2 times a weeks / two weeks / each group. Self-Development training program consists of identification of self-development elements, self-identification I, self-identification II, and human relationship. Two-weeks before and 4-weeks after the training, subjects completed the questionnaires. Analysis was done by SPSS PC 10.0 for percentile, mean, standard deviation, paired t-test and correlation. Results : The results of this study showed that the Self-Identity had not significant differences before and after Self-Development Training. When compared 5 subscales, self-assertiveness is significant difference, and goal-directedness has the lowest score before and after training. 11(46%) of head nurses mean scores at the 4 weeks after training were slight higher. Conclusion: On the basis of the finding, the researcher makes the following conclusion. This study is one step towards understanding the impact of Self-Identity for the head nurses. It would be beneficial to replicate this study with larger, more diverse samples.