Earticle

Recently, there is a substantial interest in implementing Business Process Management System(BPMS) among enterprises with the purpose of business process innovation. BPMS redesigns and coordinates business processes in terms of both automated steps and human involvement in order to maximize the value of both involved people and systems. The reason why BPMS is getting attention from top managers is that it has the possibility to optimize the business processes by cycling the process of modeling, execution, monitoring, evaluation, and redesigning work processes. Thus, it has created high expectations about not only productivity improvement but also business process innovation. However. having an innovative nature, which is used for process innovation, BPMS implementation has great potential to stir up employee resistance. The analysis and the discussion about the prevention of the resistance against IS(Information Systems) is important because IS change the way people work and also alter the power structure within the organization, in general. The purpose of this study is to investigate factors that have an impact on the effective adoption of BPMS at the enterprise level. To find out these factors, this study considers two characteristics of BPMS: First. BPMS shares some characteristics with other enterprise-wide IS such as ERP. Second, it has special BPMS-specific characteristics. Due to the lack of previous research on BPMS adoption, interviews were carried out with IT-consultants and CIOs who conducted BPMS projects previously to find out BPMS-specific features that would make BPMS unique when compared to other enterprise-wide IS. As a result, the monitoring function was chosen as the main BPMS-specific factor. Thus, this paper reviewed studies both on enterprise-wide IS adoptions, which applied Technology Acceptance Model (TAM) and secondly on computer based monitoring to find out factors that would influence the employees' perception on the monitoring function of BPMS. Based on the literature review, the study suggested three factors that would have an impact on the employee's perception of the monitoring function: fairness of enterprise evaluation system, fairness of the boss, and self-efficacy of their work. Three factors that would impact the enterprise-wide IS adoption were also set: the shared belief in the benefit of BPMS, training, and communication. Then, these factors were integrated with TAM. Structural equation modeling was used to test hypotheses, out factors that would impact the employees' perception on the monitoring function of BPMS. Based on the literature review the study suggested three factors that would have an impact on the employee's perception of the monitoring function: fairness of enterprise evaluation system, fairness of the boss, and self-efficacy of their work. Three factors that would impact the enterprise-wide IS adoption were also set: the shared belief in the benefit of BPMS, training, and communication. Then, these factors were integrated with TAM. Structural equation modeling was used to test hypotheses. The data analysis results showed that two among three monitoring function related factors - enterprise evaluation system and fairness of the boss - were significant. This implies that employees would worry less about the BPMS implementation as long as they perceive the monitoring results will be used fairly for their performance evaluation. However, employees' high self-efficacy on their job was not a significant factor in their perception of the usefulness of BPMS. This is related to cases that showed employees resisted against the information systems because they automated their works (Markus, 1983). One specific case was an electronic company, where the accounting department workers were requested to redefine their job because their working processes were automated due to BPMS implementation.

With the development of CNC(Computer Numerical Control) and communication technology, the connotation and functions of Distributed Numerical Control have been greatly enlarged. In this study, we develop and implement a Distributed Numerical Control system that has real time and multi-tasking operation capability for the machining cell with various NC(Numerical Control) and CNC machines. With the consideration of economy, generalization and extension, this system is interfaced with Shop Floor Control System, Machine Control System and Tool Preparation System using advanced networking method. In the implementation phase, we use the ORACLE DBMS (Database Management System) as the DBMS and Microsoft Visual C++ as the programming tools.

In the Wireless Sensor Network, a new MAC protocol: one-persistent CSMA protocol with monitoring functions and multichannel mechanism for Wireless Sensor Network based on conflict resolution algorithm is proposed. Protocol from the system throughput, collision rate, better service quality for higher priority and other aspects improve the traditional CSMA system to perfect the controllability of the system, making the system more stable for the big data, better adapted to different business. The average cycle method is used to derive rigorous mathematical expressions of specific relevant parameters. Using MATLAB simulation tool for the accuracy of the agreement is demonstrated.

This paper presents a new sensor network MAC protocol: the multi-channel p - persistent CSMA protocol with monitoring function (ACKPPCMA) from the perspective of improving the channel utilization and meeting the demand of the high QoS for high priority. Analyze the model by using the average cycle method. The Computer simulation results verify the correctness of the theory. Has some practical significance.

In this paper, we present a smart grid monitoring system connected with electric vehicle charging system using anti-islanding method. Electric vehicles can be charged through remote control of smart grid monitoring system and the charging process may be more stable and more efficient by virtue of wireless communication between the Local Area Module and End Modules. It is illustrated by some experiments that electric vehicle charging process continued without any serious fault even though the islanding phenomena occurred in the grid if the presented monitoring system was applied to the smart grid system.

Breathing is a crucial human process that requires easy and accurate monitoring. There are several available breathing monitoring methods which are based on strapping subjects with special sensors for monitoring their breathing rate. The results of these methods have been acceptable but connecting people with such equipment is awkward, especially in the case of monitoring the breathing of infants who have tiny and sensitive bodies. Therefore, providing a noncontact method is a good alternative. Thermal imaging is one of the latest non-contact passive techniques that are used for monitoring and controlling different human processes, including breathing. In this paper, a thermal image feature extraction method with neural network based classification system is developed to be used as part of a breathing monitoring system. The classification is based on matching the entries of mixed breathing images, either breath-in or breath-out images, with their desired values. The extracted breathing image features are used as entries for the neural network classifier. The neural network based model has produced promising classification results. Additionally, it has been possible to calculate the breathing rate of each subject from the classification results.

In this paper, for the purpose of designing an real-time unmanned monitoring system, the YOLOv5s (small) object detection model was applied on the NVIDIA TX2TM AI (Artificial Intelligence) edge computing platform in order to design the fundamental function of an unmanned monitoring system that can detect objects in real time. YOLOv5s was applied to the our real-time unmanned monitoring system based on the performance evaluation of object detection algorithms (for example, R-CNN, SSD, RetinaNet, and YOLOv5). In addition, the performance of the four YOLOv5 models (small, medium, large, and xlarge) was compared and evaluated. Furthermore, based on these results, the YOLOv5s model suitable for the design purpose of this paper was ported to the NVIDIA TX2TM AI edge computing system and it was confirmed that it operates normally. The real-time unmanned monitoring system designed as a result of the research can be applied to various application fields such as an security or monitoring system. Future research is to apply NMS (Non-Maximum Suppression) modification, model reconstruction, and parallel processing programming techniques using CUDA (Compute Unified Device Architecture) for the improvement of object detection speed and performance.

본 논문에서는 무선 센서 네트워크를 활용하여 스마트그리드와 전기자동차의 충전 시스템 연계과정을 효과 적으로 파악하고, 안티아일랜딩 기능을 적용하여 전기자동차 충전계통과 연계된 스마트그리드 모니터링 시스템을 제 안한다. 스마트그리드 모니터링 시스템은 지그비(ZigBee)통신을 통해 무선으로 전력 모니터링 데이터를 수신하며, 별 도의 물리적 연결 없이 원격 제어를 통해 전기자동차의 충전제어가 가능하다. 또한, 그리드 내 독립전원에 아일랜딩 현상이 발생하면, 아일랜딩 현상이 발생한 독립전원을 즉시 감지하여 차단하고 다른 독립전원으로 대체하여 그리드 의 안정적인 운용을 하도록 하며, 실험을 통하여 스마트그리드 모니터링 시스템의 성능을 확인한다.

In this paper, we present a smart grid monitoring system connected with electric vehicle charging system using anti-islanding method. Electric vehicles can be charged through remote control of smart grid monitoring system and the charging process may be more stable and more efficient by wireless communication between the Local Area Module and End Modules. It is illustrated by some experiments that electric vehicle charging process may not be interrupted without any serious fault even though the islanding phenomena occurred in the grid if the presented monitoring system was applied to the smart grid system.