• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.79-84
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• 2016

A novel diagnosis scheme PCADP(personalized computer aided diagnosis probability) is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method based on ontology and it makes the bio-data analysis just a 'process' in the Smart healthcare service system. In addition, we offer a semantics modeling of the smart healthcare ontology framework in order to describe smart healthcare data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has real-time processing, characteristics of flexible structure, easy monitoring of decision process, and continuous improvement.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.187-195
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• 2019

In the fourth industrial revolution era, various commercial smart platforms for smart system implementation are being developed and serviced. However, since most of the smart platforms have been developed for general purposes, they are difficult to apply / utilize because they cannot satisfy the requirements of real-time data management, data visualization and data storage of smart factory system. In this paper, we implemented an open source based smart manufacturing big data platform that can manage highly efficient / reliable data integration for the diagnosis diagnostic system of manufacturing robots.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.207-212
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• 2008

There are several sensors in the liquid cargo ship. In the liquid cargo ship, we can get values from various sensors that are level sensor, temperature sensor, pressure sensor, oxygen sensor, VOCs sensor, high overfill sensor, etc. It is important to guarantee the reliability of sensors. In order to guarantee the reliability of sensors, we have to study the diagnosis of sensor fault. The technology of smart sensor is widely used. In this paper, the technology of smart sensor is applied to diagnosis of level sensor fault for liquid cargo ship. In order to diagnose sensor fault and find the sensor position, in this paper, we proposed algorithms of diagnosis of sensor fault using independent sensor diagnosis unit and self fault diagnosis using sensor modeling. Proposed methods are demonstrated by experiment and simulation. The results show that the proposed approach is useful. Proposed methods are useful to develop smart level sensor.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.25-38
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• 2019

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.809-816
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• 2008

With increasing demands for reducing cost of maintenance which can detect machine fault automatically; low cost and intelligent functionality sensors are required. Rapid developments, in semiconductor, computing, and communication have led to a new generation of sensor called "smart" sensors with functionality and intelligence. The purpose of this research is comparison of machine fault classification between general analyzer signals and smart sensor signals. Three types of sensors are used in induction motors faults diagnosis, which are vibration, current and flux. Classification results are satisfied.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.160-167
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• 2018

In this paper, a black box, which is provided the reliability and user safety of home battery energy storage system connected with solar energy generation, is developed. In the developed scheme, a status and diagnosis data of battery management system, power conditioning system, solar energy generation and grid is measured. This status and diagnosis data is stored and displayed in the developed black box. In addition, this status and diagnosis data is stored and displayed in a monitoring system and a smart phone of user. A performance evaluation of the developed black box is carried out using emulator of home battery energy storage system connected with solar energy generation. Consequently, the developed black box is proved its superiority of the reliability and user safety.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.285-292
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• 2016

By adding short-range wireless communication function such as Bluetooth and Wi-Fi to the last vehicle in conjunction with a smart phone, a modern automobile is becoming entertainment screen to determine a variety of information such as car location information, diagnosis information, etc. through the ECU vehicle electronic control unit. In this study, by utilizing short-range communications capability of the on-board diagnostic devices and smart phones in association with the on-board diagnostics, compatibility tests among a number of smart phone models, Bluetooth and NFC(Near Field Communication) were carried out and those results were analyzed. Furthermore, composition of on-board diagnostic device having Bluetooth and NFC interface function and the fault diagnosis function were implemented, and fault diagnosis debugging program was developed. In addition, fault diagnosis data of the vehicle via the OBD-II interface was extracted. Finally, the on-board diagnostics CAN Protocol implementation has been proposed, and the results of work was analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2741-2749
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• 2009

There have been many researches to detect and recover from faults on smart home environment, because these faults should lower its reliability. while, most of these researches have addressed functional defects of devices or software malfunction, few attempts have been made to deal with faults which may occur due to the inter relationships among devices. In this paper, we define the relationships among devices as rules, and propose a method for generating fault diagnosis knowledge which defines the symptoms and causes of faults. We classify the contexts of devices into two sets, depending on whether it satisfies the rules or not. when this method is applied to smart home environment, it is feasible not only to detect faults that may occur due to the relationships among devices but to identify their causes at real time.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.55-56
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• 2015

A smart meter is a kind of electronic meters that measures and records consumption of electric energy in intervals of an hour or less and transmits that information to the remote places. AMI provides two-way communication path between utilities and consumers and should be able to support smart grid's new functionalities such as demand-response actions and real time pricing. The main objective of this paper is to provide a new diagnosis method and system for testing of smart meters in AMI neighborhood area network.