• Safety and Health at Work
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• v.11 no.1
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• pp.26-32
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• 2020

Background: Hong Kong's construction industry currently faces a manpower crisis. Blue-collar workers are a disadvantaged group and suffer higher levels of chronic diseases, for example, cancer, than the wider population. Cancer risk factors are likely to cluster together. We documented prevalence of cancer-associated lifestyle risk behaviors and their correlates among Hong Kong construction workers. Methods: Data were collected from workers at 37 railway-related construction worksites throughout Hong Kong during May 2014. Tobacco use, alcohol consumption, unbalanced nutrition intake, and physical inactivity were included in the analysis. Latent class analysis and multivariable logistic regression were performed to identify the patterns of risk behaviors related to cancer, as well as their impact factors among construction workers in Hong Kong. Results: Overall, 1,443 workers participated. Latent class analysis identified four different behavioral classes in the sample. Fully adjusted multiple logistic regression identified age, gender, years of Hong Kong residency, ethnicity, educational level, and living status differentiated behavioral classes. Conclusion: High levels of lifestyle-related cancer-risk behaviors were found in most of the Hong Kong construction workers studied. The present study contributes to understanding how cancer-related lifestyle risk behaviors cluster among construction workers and relative impact factors of risk behaviors. It is essential to tailor health behavior interventions focused on multiple risk behaviors among different groups for further enlarging the effects on cancer prevention.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5C
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• pp.527-539
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• 2009

In this paper, we propose an asynchonous ICI (Inter-Cell Interference) mitigation techniques for 3GPP LTE MIMO-OFDMA down-link receiver. An increasing in symbol timing misalignments may occur relative to sychronous network as the result of BS (Base Station) timing differences. Such symbol synchronization errors that exceed the guard interval or the cyclic prefix duration may result in MAI (Multiple Access Interference) for other carriers. In particular, at the cell boundary, this MAI becomes a critical factor, leading to degraded channel throughput and severe asynchronous ICI. Hence, many researchers have investigated the interference mitigation method in the presence of asynchronous ICI and it appears that the knowledge of the SCM (Spatial Covariance Matrix) of the asynchronous ICI plus background noise is an important issue. Generally, it is assumed that the SCM estimated by using training symbols. However, it is difficult to measure the interference statistics for a long time and training symbol is also not appropriate for MIMO-OFDMA system such as LTE. Therefore, a noise reduction method is required to improve the estimation accuracy. Although the conventional time-domain low-pass type weighting method can be effective for noise reduction, it causes significant estimation error due to the spectral leakage in practical OFDM system. Therefore, we propose a time-domain sinc type weighing method which can not only reduce the noise effectively minimizing estimation error caused by the spectral leakage but also implement frequency-domain moving average filter easily. By using computer simulation, we show that the proposed method can provide up to 3dB SIR gain compared with the conventional method.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.5
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• pp.30-37
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• 2015

The study for enhancing the data transmission rate of the next generation wireless communication system using MIMO system operating in the frequency selective fading environment is currently actively conducted. Mixed signal from each transmitted antennas are received at antennas. The training signal with orthogonal property is needed to separate the mixed signal and enable to estimate channel and time synchronization. In this paper we introduce several training sequences used in MIMO communication system and proposed the modified WeCAN sequence with good auto-correlation property in interested area. We compared auto-correlation property of each sequence via simulation and compared the performance of sequences in doppler shift and multipath fading channel.

• International Journal of Industrial Entomology and Biomaterials
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• v.6 no.2
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• pp.171-178
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• 2003

Four thermo-tolerant lines of silkworm, Bombyx mori, (L.) viz., A HT, B HT (Chinese type) and F HT, G HT (Japanese type) were evolved by utilizing the breeding resource material (identified from initial screening at a temperature of 31 ${\pm} 1^{\circ}C$ and relative humidity 85 ${\pm}$ 5%) through conventional breeding. These tolerant lines were crossed with productive breeds and forty four hybrids were evaluated on eight economic traits by the Multiple Trait Evaluation Index Method. Ten hybrids were short-listed based on the average evaluation index value larger than 50 for eight economic traits studied. The identified ten hybrids recorded higher index values (＞ 50) for most of the traits studied. Single hybrid G ${\times}$ CSR12 indicated average index value larger than 50 for six traits viz., pupation number (58), cocoon weight (67), shell weight (65), average filament length (74), raw silk % (69), reelability % (51) except for shell ratio % (41). The standard deviation of the cocoons in the above hybrid was 8.41 in the hybrid cocoon length and width measurement. However, two selected hybrids viz., A ${\times}$ CSR5 and G ${\times}$ CSR13 recorded average index value larger than 50 for all the traits viz., pupation number (57, 60), cocoon weight (50, 54), shell weight (56, 57), shell ratio percentage (59, 53), average filament length (55, 60), raw silk percentage (63, 67) and reelability percentage (53, 53). The standard deviation of the cocoons in the two selected hybrids viz., A ${\times}$ CSR5 and G ${\times}$ CSR13 was 8.41 and 8.06 respectively in the cocoon length and width measurement.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.551-553
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• 1999

This paper explores the utility of a new classifier fusion approach to discrimination. Multiple classifier fusion, a popular approach in the field of pattern recognition, uses estimates of each individual classifier's local accuracy on training data sets. In this paper we investigate the effectiveness of fusion methods compared to individual algorithms, including the artificial neural network and k-nearest neighbor techniques. Moreover, we propose an efficient meta-classifier architecture based on an approximation of the posterior Bayes probabilities for learning the oracle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.407-415
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• 2019

Hearing - impaired children who have difficulty hearing and hearing go through pronunciation training. The purpose of this study is to provide the pronunciation training system based on digital contents that can be used for repeated hearing training while maintaining interest in hearing - impaired children. For this purpose, we conducted an interview survey for users and experts. Based on the results, we developed a digital content based pronunciation training system. Finally, to verify the effect of the digital service implemented, the user test was conducted for the hearing - impaired children. As a result of the interview, repeated training and interest factors were found to be essential factors affecting pronunciation training. In implementing digital services, we have used fairy tales and a variety of interactive elements to derive children's interests and designed a user flow that can train multiple words and sentences for effective repetition training. As a result of the test, this digital content was evaluated positively.

• Physical Therapy Korea
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• v.7 no.3
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• pp.81-89
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• 2000

The purpose of this study was to determine the effect of electrical stimulation biofeedback on motor learning of quadriceps muscle isometric exercise in 3 patients who have undergone total knee replacement surgery. A multiple baseline design across subjects was used. The electrical stimulation biofeedback was provided with each patient during quadriceps isometric exercise, which last 10 to 14 sessions with 10 repetitions each sessions. After training patients received 4 retention tests. Maximum muscle activity was measured pre- and post- electrical stimulation biofeedback training and retention test to evaluate the effect of biofeedback training. Maximum isometric muscle activity of quadriceps was increased after electrical stimulation biofeedback training in all subjects. The results indicate that a electrical stimulation biofeedback training is a useful method to improve motor learning of quadriceps isometric exercise in total knee replacement.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.577-582
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• 2020

The performance of a teacher has an important role in the success of education in general. This study aims to determine the factors that affect the decline in teacher performance in one of the junior secondary schools in Indonesia. Based on the literature review, four exogenous variables were identified, namely, training, utilization of information technology, intellectual intelligence, and emotional intelligence. This study uses primary data, collected from a questionnaire distributed to respondents. The questionnaire items are measured using a Likert scale. The sample in this study were all teachers at MTS Darul Falah Sirahan, totaling 32 people. The analysis technique used in testing the hypothesis of this study is multiple regression analysis. Statistical product and service solutions are used as analysis tools. The results of this study indicate that only the variable 'utilization of information technology' has a positive and significant effect. However, the variables 'training,' 'intellectual intelligence,' and 'emotional intelligence' did not have a significant effect. This finding contradicts the literature in general. Therefore, this study recommends improving training methods, both those carried out internally by schools and by related agencies, and schools still need to optimize guidance and potential for teacher's intelligence in improving performance.