• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.19-36
• /
• 2011

Since the ambitious declaration of the vision of the Semantic Web, a growing number of studies on semantic search have recently been made. However, we recognize that our community has not so much accomplished despite those efforts. We analyze two underlying problems : a lack of a shared notion of semantic search that guides current research, and a lack of a comprehensive view that envisions future work. Based on this diagnosis, we start by defining semantic search as the process of retrieving desired information in response to user's input using semantic technologies such as ontologies. Then, we propose a classification framework in order for the community to obtain the better understanding of semantic search. The proposed classification framework consists of input processing, target source, search methodology, results ranking, and output data type. Last, we apply our proposed framework to prior studies and suggest future research directions.

• Journal of Korea Multimedia Society
• /
• v.11 no.10
• /
• pp.1376-1384
• /
• 2008

Numerous researches on network self-configuration have been conducted on Wireless Sensor Network (WSN) and Ad Hoc network but the results have not been applied yet for factory automation. This paper presents development of intelligent process management systems conducting process monitoring and control irrelevant to physical position of a plant operator. The systems are indispensable for diagnosis of rotating machines which cannot exploit wired network. The system was implemented in a Personal Digital Assistant (PDA) using Bluetooth communication protocol. When a mobile terminal reaches to communication range of the process management server, the server detects the mobile terminal to reconfigure plant network automatically. The server authenticates a user of the terminal to download and installs monitoring and control program considering authorized level of the user. If the terminal leaves communication range of the server, it deletes the node from the network and removes the program automatically to save resources and prevent security problems such as missing terminal. Operation of developed functions was verified in a testbed emulating steel-making plant.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.5
• /
• pp.373-383
• /
• 2015

This paper proposes a decision support system to recognizing retinal diseases. This paper uses a smartphone platform and cloud computing as the base of the system. A microscopic lens is attached int' the smartphone camera to capture the user retinal image for recognizing the user's retinal condition. An application is assembled in computer and then installed in to the smartphone. The application role is to connect between the system in smartphone and system in cloud, the application will send the retinal image to the cloud system to be classified. The paper uses OCFE (optimized classifier based on feature elimination) algorithm as the classifier. The retinal image is trained using combination of two ophthalmology databases DIARETDB1 v2.1 and STARE. Therefore, this system average accuracy is 88%, while the average error rate is 12%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.513-516
• /
• 2013

In this paper, we present an information collecting system in medical information management domain. Our proposed system performs a systemized process, consisting of collection, transmission, and management, to develop intelligent medical information system and medical big data processing system. Our information collecting system consists of low-power biomedical sensors, biomedical information collecting devices, and storage systems. Currently, almost biomedical information of patients is collected manually by employees like nurses and medical doctors. Therefore, collected biometric data can be error-pronoun data. Since there is a lack to make big data of medical information, it is difficult to enhance the quality of medical services and researches. Accordingly, through our proposed system, we can overcome the problems like error-pronoun biometric data. In addition, we can extremely extend the area of collectable biometric data. Furthermore, using this system, we are able to make a real-time biomedical analysis system, like a real-time patient diagnosis system, and establish a strategy to against future medical markets changing rapidly.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.10
• /
• pp.1949-1954
• /
• 2016

Smart grid technology can be called grid techniques to improve the efficiency of the electric power by exchanging bidirectional information of electric power with real-time between electric power suppliers and consumers. Recently, the solar power generation system is being applied actively. However the solar power system has several problems leading to reduce overall electricity generation, because the difficult of the diagnosis and the solar power system failure such as PV(PhotoVoltaics) and inverter. In order to build an efficient smart grid, a stable electric power energy requirements capture and management and early fault detection is essentially required in solar power generation system. In this paper, it is designed to monitor the operating status of the solar power monitoring system from a remote location through a RS-485 or TCP/IP communication module to monitoring the output of solar power energy and abnormal phenomenon, to developing the measurement module and to transfer measured data.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.4 s.36
• /
• pp.29-36
• /
• 2005

As computing environments are more rapidly developed, an adaptive and intelligent services using post PC such as PDA. Laptop, and Tablet PC in case of rounding and examining patients are highly demanded. The objective of this study is to design and implement a context-awareness support system based on voice service for medical environments. To achieve it, we propose a context middleware which plays an important role in recognizing a client with PDA by using a Bluetooth wireless communication technology as well as in executing an appropriate execution module, like delivery for diagnosis information of patients, according to the staff's context acquired from a context server. In addition, the context server functions as a manager that efficiently stores context information such as client's current status, physical environment, and device resources into a database server. Finally, for verifying the usefulness of the proposed system, we develop an application system which provides voice playing services for notification of other physicians through our context middleware.

• BMB Reports
• /
• v.55 no.11
• /
• pp.547-552
• /
• 2022

Sorafenib, originally identified as an inhibitor of multiple oncogenic kinases, induces ferroptosis in hepatocellular carcinoma (HCC) cells. Several pathways that mitigate sorafenib-induced ferroptosis confer drug resistance; thus strategies that enhance ferroptosis increase sorafenib efficacy. Orphan nuclear receptor estrogen-related receptor γ (ERRγ) is upregulated in human HCC tissues and plays a role in cancer cell proliferation. The aim of this study was to determine whether inhibition of ERRγ with DN200434, an orally available inverse agonist, can overcome resistance to sorafenib through induction of ferroptosis. Sorafenib-resistant HCC cells were less sensitive to sorafenibinduced ferroptosis and showed significantly higher ERRγ levels than sorafenib-sensitive HCC cells. DN200434 induced lipid peroxidation and ferroptosis in sorafenib-resistant HCC cells. Mechanistically, DN200434 increased mitochondrial ROS generation by reducing glutathione/glutathione disulfide levels, which subsequently reduced mTOR activity and GPX4 levels. DN200434 induced amplification of the antitumor effects of sorafenib was confirmed in a tumor xenograft model. The present results indicate that DN200434 may be a novel therapeutic strategy to re-sensitize HCC cells to sorafenib.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.27-40
• /
• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.9
• /
• pp.191-203
• /
• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• The KIPS Transactions:PartB
• /
• v.15B no.2
• /
• pp.137-146
• /
• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.