• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.331-342
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• 2020

Purpose: This study concerns the cost of safety management in order to prevent accidents and disabilities in the construction industry. The purpose of the Chapter is to draw up problems and improvement measures for these safety management costs. Method: A detailed questionnaire was developed for 20 construction safety managers for the research. This questionnaire was analyzed and implemented by the Expert Group Interview (FGI) analysis method. Results: In order to understand the safety management cost system, alternatives such as preparing a standard manual, implementing regular on-site training, and establishing a monitoring system for the safety management cost were derived. In addition, there is a need to ensure autonomy in the execution of flexible costs so that they can be deployed immediately in case of emergency to improve the 'efficiency and necessity of the safety negligence system'. And it was analyzed as items that should be improved due to excessive demand for documentation. Conclusion: Through this study, problems should be recognized for the improvement of the construction safety management cost system. And it will have to come up with policy-based and institutional-improvement measures for experts.

• Journal of Forest and Environmental Science
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• v.32 no.2
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• pp.189-195
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• 2016

Logging equipment and method have a major influence on harvesting productivity and cost. This study investigated the productivity and operational costs of tree-length cable yarding system using HAM300, a domestically developed tower yarder. We tested HAM300 for thinning operation in Pinus densiflora stands at Gangreung, Gangwon-do on April, 2014. To assess the productivity we conducted time study for each stage of the operation. When the average time/cycle was examined for each stage of the operation, the longest was for yarding (241 sec), followed by delimbing (237 sec), felling (153 sec), and processing (103 sec). Furthermore, productivity for felling was $8.6m^3/hr$, followed by delimbing ($5.1m^3/hr$), yarding ($3.5m^3/hr$), and processing ($8.1m^3/hr$). The total cost for the tree-length logging system was $58,446won/m^3$, of which the majority was incurred by the yarding cost at $46,217won/m^3$ (79.3%), whereas the lowest cost was for felling at $2,359won/m^3$ (4.1%). We suggest that it is necessary to foster specialized operators and provide training in operating the tower yarder thereby implementing efficient harvesting system resulting from low-cost yarding.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.4
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• pp.433-440
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• 2016

In order to construct a system for big data processing, one needs to configure the node by using network equipments to connect multiple computers or establish cloud environments through virtual hosts on a single computer. However, there are many restrictions on constructing the big data analysis system including complex system configuration and cost. These constraints are becoming a major obstacle to professional manpower training for big data areas which is emerging as one of the most important national competitiveness. As a result, for professional manpower training of big data areas, this paper proposes a Raspberry Pi Board based educational big data processing system which is capable of practical training at an affordable price.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.689-696
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone are used for identifying trouble spots of software system in analysis or design phases. Many criticality prediction models for identifying fault-prone modules using complexity metrics have been suggested. But most of them need training data set. Unfortunately very few organizations have their own training data. To solve this problem, this paper builds a new prediction model, KSM, based on Kohonen SOM neural networks. KSM is implemented and compared with a well-known prediction model, BackPropagation neural network Model (BPM), considering internal characteristics, utilization cost and accuracy of prediction. As a result, this paper shows that KSM has comparative performance with BPM.

• Asian-Australasian Journal of Animal Sciences
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• v.6 no.4
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• pp.509-514
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• 1993

The experiment was conducted to determine the effect of different fiber levels on milk production of crossbred Holstein milking cows fed urea-treated rice straw (UTS) as a roughage. Eight cows were allotted into 2 squares of 4 cows each with 4 treatments by a balanced design. The treatments were 17%, 22%, and 24% crude fiber (CF) diets and Thai feeding system (free choice of roughage and 1 kg of concentrates/2 kg of milk) as a control. Body weight change was not significantly different among the treatments during the experiment. Milk production (4% FCM) and milk protein content wee not different among the treatments, but milk fat content was low in the 17% CF group and high in the control group. Cows fed the 17% CF diet consumed less UTS and more concentrates than the others, and consequently total DM intake was not different among the treatments. The feed conversion ratio was significantly higher in the control. Feed cost per kg milk was lowest in the control and highest in the 17% CF diet. The fiber content of the diet would be more than 17%, preferably 22-24% for normally producing Thai crossbred Holstein cows when the UTS was fed as a main roughage source.

• ETRI Journal
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• v.17 no.1
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.125-144
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• 2009

Korean logistics industry have been focused on transportation business. However, with the expansion of the electronic commerce and on-line shopping, delivery service is now dramatically growing. Despite the expansion of logistics market, the domestic logistics industry have significant structural problems such as low productivity comparing with the advanced countries, relatively high cost and shortage of human resources and lack of professionalism of people in the industry. Logistics companies reallocate employees, use subcontractors, expand consignment and training the employees to overcome the labor shortages but it has some limits. In recognition of the importance of labor in the logistics industry, financial support and investment have increased. Logistics companies tend to hire consultants, set up logistics department or R&D center in order to establish highly productive logistics process and system so it is viewed that there will be considerable demands of human resources in the logistics industry. This study indicates implications and development direction of human resources in the logistics industry by looking into prospect and characteristic of the industry, employment status, training programs and qualification requirements.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.352-361
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• 2013

Combining multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) with a massive number of transmit antennas (massive MIMO-OFDM) is an attractive way of increasing the spectrum efficiency or reducing the transmission energy per bit. The effectiveness of Massive MIMO-OFDM is strongly affected by the channel state information (CSI) estimation method used. The overheads of training frame transmission and CSI feedback decrease multiple access channel (MAC) efficiency and increase the CSI estimation cost at a user station (STA). This paper proposes a CSI estimation scheme that reduces the training frame length by using a novel pilot design and a novel unitary matrix feedback method. The proposed pilot design and unitary matrix feedback enable the access point (AP) to estimate the CSI of the signal space of all transmit antennas using a small number of training frames. Simulations in an IEEE 802.11n channel verify the attractive transmission performance of the proposed methods.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.245-250
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• 2018

Ab Image Processing can be applied to the various fields nowadays. The healthcare, which people may have interest, is one of those fields. The healthcare program which fitness equipments are used requires the correct posture of body. However, people usually does exercise by oneself with various reasons such as cost to hiring personal trainer. In this case, the effectiveness of training should not be guaranteed. Moreover, reverse effect may be produced. This paper is an implementing a correcting posture program by using image processing techniques, which people can use exercise training by oneself. It is expected that people can have a personal trainer who coaches the correct postur through this program.

• 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.