This paper deals with a joint optimizing problem of maintenance and spare provisioning for a unit with random working time. The unit operates in a random cyclic manner: the unit executes jobs successively, and the job has a variable working cycle. A maintenance policy is applied as follows. The unit is preventively replaced by a new one at a predetermined time  or at the end of working time, whichever comes first. When the unit fails before preventive replacement, it is immediately replaced by a new one. An inventory policy for the unit is applied as follows: Some quantity is ordered for spare provisioning and used when the unit fails or preventively replaced. The spares are immediately replenished when the stock comes to zero. The cost rates per unit time under the joint maintenance and inventory policy is obtained as a function of preventive maintenance time and ordering quantity. It is shown that under some conditions there exist finite optimal values of the policy variables. Numerical examples are provided to demonstrate the results.

Due to the recent economic situation, there are difficulties in various industrial fields, and the construction industry also suffers from difficulties and competitiveness. For this problem, it is necessary to present concrete action programs to strengthen the capacity of construction machine training. This study is an extended technology acceptance model, which empirically analyzes related factors affecting behavioral intention, and analyzes differences among groups as training variables. Using the questionnaire method, 594 people were selected and extracted through SPSS / WIN 20.0 and AMOS 20.0 package programs. In addition, as a result of the multi-group comparison analysis (MSEM), practical equipment and training programs should be prepared to meet the objectives of the construction equipment manufacturer training institution. It can be used as a research resource for teaching practice.

method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM) by static method, and kalman filtering model (KFM) by dynamic method with existing accident data in agriculture. In this paper, microsoft foundation class (MFC) software of Visual Studio 2008 was used to minimize SSE of the occupational accident rate and the fatal accident rate. The minimum value of SSE in the agriculture was found in 0.4970 and 17.8381 in the occupational accident rate and fatal accident rate, respectively. Accordingly, ARIMA model in the agriculture are ideally applied in the accident rate and fatal accident rate. Finally, this paper provides very efficient and strategic method for prevention of accident in agriculture through the trend of determined prediction model and analysis of accident data.

Well-established reliability prediction methodology could lead to unbiased good reliability prediction results during the life cycle of product or system which are essential part of reliability management for the correct decision-making. The purpose of this paper is to examine and analysis methodologies and models of reliability prediction in order to provide an effective prediction methodology on reliability from initial design stages to the full production stages. Depending upon censoring of failure data, methodologies could be classified into statistical empirical prediction method through lifetime distribution function, physics of failure method based on physical models and hybrid method.
This paper provide a concise summary of standards-based reliability prediction methods and suggest model-based reliability prediction models which are capable of applying the current technology with no applicable failure rate database

values and thresholds were set. And then data preprocessing was performed using linear interpolation. The proposed model learns based on the steady-state data of manufacturing facilities. Then, the input vector of preprocessed data was sampled using a hybrid long short term memory (H-LSTM) circulatory neural network model and used for learning. In order to verify the proposed method, data were collected based on two fault conditions and the experiments were performed based on the two fault conditions. The degree of abnormality is expressed by measuring the root mean square error(RMSE) between the output of each state data and the prediction result. The experiments verified the accuracy of the proposed failure prediction technique.

There are many works on resolving delays in disaster communication, which are a critical concern in disaster IoT systems, but some concerns remain. We focus on delays that are derived from the communication architecture of disaster IoT systems, which is composed of both unicast and multicast communications, and propose a smart push notification method for improving delay over third-party push server in disaster IoT systems for detection of water flood during an emergency. The proposed method determines the exact time point to send a push notification and results in improved disaster preparedness. This work implements the proposed method on a third-party push notification server, and the experiment results show that the proposed method with the smart push notification algorithm reduces delay by 20 % and error rate in the expected future measurements by 4 % compared to the legacy methods.

We examined the impact on management performance and the moderating effect of policy fund in the process, taking into consideration entrepreneurial orientation and innovative activity. Innovativeness and proactiveness have a direct impact on financial and non-financial performance. On the other hand risk-taking is not statistically significant. When proactiveness affects financial performance and non-financial performance, policy fund has been shown to have an adjustment effect. And when risk-taking affects financial performance and non-financial performance, policy funds has been shown to have a moderated effect. Since this study conducted detailed analysis up to the lower factors regarding the impact of entrepreneurship orientation on innovation activities and corporate performance, it is believed to be helpful in future related research, and that the research results will help establish or improve effective government support measures.

In this paper, we propose a method based on the support vector regression to predict the gas energy consumption in a conditioning equipment. We analyzed the time series of gas consumption by using auto and partial correlation functions and found that the gas consumption contains a non-linear correlation in the time series. Based on this finding, we adopted the support vector regression as a non-linear model for the prediction of the gas consumption. By tuning two parameters in the model and introducing the cross validation, we demonstrated that the proposed model predicted the gas consumption with the relative errors and the average errors less than 1% and 0.5Nm3/h 3.2 Nm3/h respectively. The results of this study can be used for the effective control of the conditioning energy in the air conditioning process.

The purpose of this study is to investigate the labor environment of IT workers in connection with the introduction of the 52-hour work per week in the domestic IT industry. The survey was conducted between June and August 2019 on 1,364 IT workers, including basic working conditions, such as working hours, breaks and welfare systems, and health and safety education. As a result of the study, the welfare system was well provided for workplaces with 500 workers, including securing exercise space, and occupational safety and health education were well conducted. 70.16 percent answered that the average working hours per week were between 8 and 10 hours per day, and there were some differences between the size of workplaces and jobs. The daily working hours were the highest for SI jobs, the lowest for management, and the H/W maintenance staff during rest hours, while the digital content workers did not have adequate rest time. In addition, it did not believe that the perception of labor unions by IT workers could actively help solve problems due to their personal performance-oriented working environment.