• Journal of the Korean Institute of Educational Facilities
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• v.25 no.6
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• pp.3-10
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• 2018

This study analyzes the average acquisition ratio between the 1st and 4th grade and the distribution and characteristics of the acquisition between the assessment items based on the case of Green Building Certification System(GBCS) for the educational facilities on the university campus. By evaluating the difference between the best scored grade (Green 1st Grade) and the general grade (Green 4th Grade), we classified the cases where the difference between grades with little or big difference. By doing this, we were able to analyze the trend that were difficult to distinguish between difficult to score and an easy task. G-SEED should be used not only as a formal system for certifying certification, but also to provide environmentally-friendly technology for buildings as well as meeting market needs. To do this, it is necessary to further refine the evaluation criteria. In addition, it is necessary to establish a qualitative evaluation system by providing detailed criteria for items that are not distinguishable among grades. It is expected that this study will be used to improve the detailed evaluation items by analyzing trends in the average acquisition rate presented in this study.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.40-49
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• 2019

Reinforcement learning has been applied to various problems in robotics. However, it was still hard to train complex robotic manipulation tasks since there is a few models which can be applicable to general tasks. Such general models require a lot of training episodes. In these reasons, deep neural networks which have shown to be good function approximators have not been actively used for robot manipulation task. Recently, some of these challenges are solved by a set of methods, such as Guided Policy Search, which guide or limit search directions while training of a deep neural network based policy model. These frameworks are already applied to a humanoid robot, PR2. However, in robotics, it is not trivial to adjust existing algorithms designed for one robot to another robot. In this paper, we present our implementation of Guided Policy Search to the robotic arms of the Baxter Research Robot. To meet the goals and needs of the project, we build on an existing implementation of Baxter Agent class for the Guided Policy Search algorithm code using the built-in Python interface. This work is expected to play an important role in popularizing robot manipulation reinforcement learning methods on cost-effective robot platforms.

• Korean Journal of Applied Biomechanics
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• v.28 no.4
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• pp.219-225
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• 2018

Objective: Epidemiological research shows that 47 to 73% of athletes suffer from recurrent ankle sprains. Joint mobilization techniques (JMT) implemented in correcting may be beneficial in the management of ankle injuries. The purpose of this study is to examine the immediate JM on ankle complex as clinical measures in individuals with chronic ankle instability (CAI) through intervention. Method: Thirteen subjects with CAI (8 males and 5 females) participated in this study. Each subject tried total four alignments (Navicular drop test: NDT, Standing rearfoot angle: SRA, Tibia torsion: TT, and dorsiflexion range of motion: DFROM). The participants were performed the 10 meter shuttle run after JMT for post-task. Finally, it was tried to compare between pre-post tasks after shuttle run. Results: SRA and DFROM after intervention showed significant differences. SRA (p=.026), and DFROM (p=.034). Conclusion: We concluded that the JMT has resulted in improvement in SRA, DFROM. Increased DFROM and varus shapes of foot would be closed kinetic chain, indicating that reduce the risk factors of ankle sprain. Future study needs to be conducted in order to measure the effects of prolonged intervention of JMT.

• Safety and Health at Work
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• v.9 no.4
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• pp.427-433
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• 2018

Background: The purpose of this study was to identify the influence of workers' perceived workload, accident experiences, supervisors' safety leadership, and an organization's safety climate on the cognitive and emotional risk perception. Methods: Six hundred and twenty employees in a variety of manufacturing organizations were asked to complete to a questionnaire. Among them, a total of 376 employees provided valid data for analysis. To test the hypothesis, correlation analysis and hierarchical regression analysis were used. Statistical analyses were conducted using IBM SPSS program, version 23. Results: The results indicated that workload and accident experiences have a positive influence and safety leadership and safety climate have a negative influence on the cognitive and emotional risk perception. Workload, safety leadership, and the safety climate influence perceived risk more than accident experience, especially for the emotional risk perception. Conclusion: These results indicated that multilevel factors (organization, group, and individual) play a critical role in predicting individual risk perceptions. Based on these results, therefore, to reduce risk perception related with unsafe behaviors and accidents, organizations need to conduct a variety of safety programs that enhance their safety climate beyond simple safety-related education and training. Simultaneously, it needs to seek ways to promote supervisors' safety leadership behaviors (e.g., site visits, feedback, safety communication, etc.). In addition, it is necessary to adjust work speed and amount and allocate task considering employees' skill and ability to reduce the workload for reducing risk perception.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Journal of IKEEE
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• v.25 no.1
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• pp.25-31
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• 2021

The conventional credit card number recognition system generally needs a card to be placed in a designated location before its processing, which is not an ideal user experience especially for people with visual impairment. To improve the user experience, this paper proposes a novel algorithm that can automatically detect the location of a credit card number based on the fact that a group of sixteen digits has a fixed aspect ratio. The proposed algorithm first performs morphological operations to obtain multiple candidates of the credit card number with >4:1 aspect ratio, then recognizes the card number by testing each candidate via OCR and BIN matching techniques. Implemented with OpenCV and Firebase ML, the proposed scheme achieves 77.75% accuracy in the credit card number recognition task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1798-1813
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• 2021

This paper proposes a positive experience design approach for Internet of Things (IoT) intelligent products to improve users' subjective well-being in the fields of artificial intelligence and big data. First, the authors selected six target users and taking the Xiaomi IoT intelligent products for the research objects and conducted a thorough observation on how the target users used IoT intelligent products in their own homes over two weeks via a home-visiting interview, group diary, and focus group. Second, they constructed an individual activities table for the participants' IoT intelligent product experience using a hierarchical task analysis (HTA). Third, two researchers sorted out the sub-tasks of happiness in the HTA table. Finally, the authors found the positive experience design approach of IoT intelligent products. The positive experience design approach of IoT intelligent products is proposed from focusing on the personal pleasure experience to individual life meaningful design and group social relationship design, including individual pleasure experience, personal goal realization, group needs satisfaction and the harmony of group relations. The paper uses the two design examples of an interactive kettle and a harmonious chair to further discuss the feasibility of the design approach. In the era of big data, it is helpful for designers to use this design approach to improve the users' sense of sustainable pleasure, achievement perception of their future goal realization, and the well-being of the group's social relationships.

• Korean Journal of Organic Agriculture
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• v.28 no.4
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• pp.555-577
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• 2020

This study analyzes the management performance and productivity of environmentfriendly farms compared to conventional farms and the trend of changes in price premium rates of environment-friendly agricultural products. And environmentfriendly farms in Jeollanam-do are surveyed for difficulties in management, proper premium rate of environment-friendly agricultural products (WTA), and tasks for promoting sales. According to the analysis results, the management performance and productivity of are low in many items, and the number of items that are on the decline or stagnant in the environment-friendly premium is making it difficult for farmers to manage. According to a farm survey, the most important task is to promote school meals for boosting sales of environment-friendly agricultural products. And 65.5% of the respondents having contract cultivation, nearly half or 41.1% of the respondents said they do not need contract cultivation or want contract cultivation for less than one year, which means that the current contract does not meet the needs of farmers. Finally, the environment-friendly premium rate based on consumer prices is generally lower than the premium rate (WTA) that farmers perceive as appropriate, so it is important to resolve the gap between the actual premium rate and the WTA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3619-3637
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• 2022

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data. Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data. The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile. Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge. Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down. This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics. Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors. The method clearly segregates impactful parameters from the rest. The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%. The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.