• Journal of Software Engineering Society
• /
• v.26 no.3
• /
• pp.53-62
• /
• 2013

Along with increasing supplies of smart devices, a proliferation of context-aware applications is came. However, acquiring contexts through sensors requires considerable energy consumption. It has became big constraints on running many context-aware applications in mobile devices having limited battery capacity. Hence, energy-efficient methods for monitoring contexts are highly required. In this paper, we propose four context monitoring methods, analyse energy consumption in each method, and provide guidelines for applying the methods. It is effective to decrease energy consumption for monitoring contexts with applying the methods. To assess the proposed methods, we implement an application that is aware of a user's motion and show quantitative comparison between each of the methods.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2003.06a
• /
• pp.1097-1100
• /
• 2003

Data Loggers for environmental monitoring are mostly dispersion in installation and systems located at long distance from monitoring system. And, it requests mostly flexible functions and high performances. that can fit to various sensor inputs, sensor interfaces and conditions or system working. In this research, we developed the micro controller based Data Logger with minimum hardware construction that allows the higher flexibility of application. Finally, we developed software function for water quality monitoring and tested in real system launched at Han river.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.61
• /
• pp.147-154
• /
• 2000

There is purpose of this research in development of monitoring/forecasting system. For monitoring/forecasting system development, we need modelling of suitable development step and need to basis data. So in this Paper, wish to develop modeling and necessary business program until begin in target company's basis data survey and construct Database and software development for monitoring/forecasting.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.5
• /
• pp.303-310
• /
• 2017

Underwater robots operating in constrained underwater environment have requirements for software systems. Firstly, it is necessary to provide reusable common software components for hardware interface of sensors and actuators that are frequently used in underwater robots. Secondly, it is required to support distributed execution environment on multiple embedded controllers. Thirdly, it is need to implement a monitoring system capable of high-speed and large-data transmission for underwater robots operating in an environment where it is difficult to check the robot status. For these requirements, we have designed the layered architecture pattern and applied several design patterns to enhance the reusability and the maintainability of software components, In addition, we overlaid the broker architecture pattern to support distributed execution environments. Finally, we implemented the underwater robot software system using ROS framework based on the software architecture design. In order to evaluate the performance of the implemented software system, we performed an experiment to measure the response time between components and the transmission rate of the monitoring data, and obtained the results satisfying the required performance.

• Industrial Engineering and Management Systems
• /
• v.10 no.1
• /
• pp.43-53
• /
• 2011

Currently, knowledge within the field of software development is largely implicit and is not formally disseminated and shared. This means that there is little improvement and regeneration of processes, and knowledge gained from previous projects is not necessarily applied to new ones. In order to turn this situation around it is necessary to take an organized approach to sharing job-related information. For this study, the authors constructed "Amalab-Project Planning Navigation System, or A-PPNS", a system for organizing accumulated knowledge related to the field of software development. More specifically, A-PPNS is a business process monitoring system and consists of the following four elements: (i) Optimized estimate support subsystem, (ii) Schedule monitoring system, (iii) QCD optimization diagnostic system, and (iv) Strategic technology accumulation system. The effectiveness of this system has been demonstrated and verified at Company A, a system integration company.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.11
• /
• pp.1463-1470
• /
• 2021

With the development of ICT technology, a monitoring system to check the status of an object to be managed in real time in various industrial fields is widely used. Existing monitoring systems implemented individual systems according to monitoring targets, but recently, monitoring systems have been implemented using open sources such as Prometheus and Grafana. When using Prometheus and Grafana, many parts become more convenient compared to the existing monitoring system development method, but there are still problems. In this paper, to solve this problem, we propose an integrated monitoring system that supports Prometheus and Grafana. The proposed system is a detailed module that collects, stores, visualizes, and manages data to be monitored, and each module is implemented so that roles can be divided and existing problems can be solved. The proposed system can conveniently manage and monitor monitoring targets stored in heterogeneous databases, and create dashboards through simple operation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3870-3884
• /
• 2020

The proposed framework of Four Layer Robust Storage in Cloud (FLRSC) architecture involves host server, local host and edge devices in addition to Virtual Machine Monitoring (VMM). The goal is to protect the privacy of stored data at edge devices. The computational intelligence (CI) part of our algorithm distributes blocks of data to three different layers by partially encoded and forwarded for decoding to the next layer using hash and greed Solomon algorithms. VMM monitoring uses snapshot algorithm to detect intrusion. The proposed system is compared with Tiang Wang method to validate efficiency of data transfer with security. Hence, security is proven against the indexed efficiency. It is an important study to integrate communication between local host software and nearer edge devices through different channels by verifying snapshot using lamport mechanism to ensure integrity and security at software level thereby reducing the latency. It also provides thorough knowledge and understanding about data communication at software level with VMM. The performance evaluation and feasibility study of security in FLRSC against three-layered approach is proven over 2³² blocks of data with 98% accuracy. Practical implications and contributions to the growing knowledge base are highlighted along with directions for further research.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.2
• /
• pp.391-394
• /
• 2006

The paper deals with the experimental GNSS receiver built at the Czech Technical University for experiments with the real GNSS signal. The receiver is based on software defined radio architecture. Receiver consists of the RF front end and a digital processor based on programmable logic. Receiver RF front end supports GPS L1, L2, L5, WAAS/EGNOS, GALILEO L1, E5A, E5B signals as well as GLONASS L1 and L2 signals. The digital processor is based on Field Programmable Gate Array (FPGA) which supports embedded processor. The receiver is used for various experiments with the GNSS signals like GPS L1/EGNOS receiver, GLONASS receiver and investigation of the EGNOS signal availability for a land mobile user. On the base of experimental GNSS receiver the GPS L1, L2, EGNOS receiver for railway application was designed. The experimental receiver is also used in GNSS monitoring station, which is independent monitoring facility providing also raw monitoring data of the GPS, EGNOS and Galileo systems via internet.

• Journal of Korea Multimedia Society
• /
• v.24 no.2
• /
• pp.233-244
• /
• 2021

Recently, several ICT-based cat studies have produced some successful results, according to academic and industry sources. However, research on the level of simply identifying the cat's condition, such as the behavior and sound classification of cats based on images and sound signals, has yet to be found. In this paper, based on the veterinary scientific knowledge of cats, a practical and academic cat monitoring and disease diagnosis system is proposed to monitor the health status of the cat 24 hours a day by automatically categorizing and analyzing the behavior of the cat with location information using LSTM with a beacon sensor and a raspberry pie that can be built at low cost. Validity of the proposed system is verified through experimentation with cats in actual custody (the accuracy of the cat behavior classification and location identification was 96.3% and 92.7% on average, respectively). Furthermore, a rule-based disease analysis system based on the veterinary knowledge was designed and implemented so that owners can check whether or not the cats have diseases at home (or can be used as an auxiliary tool for diagnosis by a pet veterinarian).

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.704-707
• /
• 2021

Pigs' aggressive behavior represents one of the common issues that occur inside pigpens and which harm pigs' health and welfare, resulting in a financial burden to farmers. Continuously monitoring several pigs for 24 hours to identify those behaviors manually is a very difficult task for pig caretakers. In this study, we propose a lightweight video-based approach for monitoring pigs' aggressive behavior that can be implemented even in small-scale farms. The proposed system receives sequences of frames extracted from an RGB video stream containing pigs and uses MnasNet with a DM value of 0.5 to extract image features from pigs' ROI identified by predefined annotations. These extracted features are then forwarded to a lightweight LSTM to learn temporal features and perform behavior recognition. The experimental results show that our proposed model achieved 0.92 in recall and F1-score with an execution time of 118.16 ms/sequence.