• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.898-901
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• 2008

Measuring instrument module for biosensor activity is developed using CMOS image sensor and sensor network. Most of living organism in water as water flea, fish, etc are frequently used as biological sensor for monitoring the water qualify. The activity of biosensor is changed by the quality of water. The developed measuring instrument module can easily interface to the existing instrument.

• Knowledge Management Research
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• v.16 no.3
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• pp.81-102
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• 2015

With the rapid development of digital media, there has been a huge change in a way of communication, a process of information diffusion and a role of traditional media. Not like mass media, social media enables users to generate and tap into the opinions of a larger world. From that reason, social media is impacting marketing strategies. However, still social media marketing researches just focus on case study, analysis of users motivation or analysis of power user's usage pattern. Word-of-mouth has always been important especially in marketing area. In social media, word-of-mouth depends on each user that's why this research focuses on individual user's activity in SNS. I defined 4 factors (produce, diffusion, network size, activity of network size enlarge) that are effect on activity and verified hypothesis by multiple regression analysis, hierarchical regression analysis and moderated multiple regression.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.666-669
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• 2008

A number of intrusion detection systems have been developed to detect intrusive activity on individual hosts and networks. The systems developed rely almost exclusively on a software approach to intrusion detection analysis and response. In addition, the network systems developed apply a centralized approach to the detection of intrusive activity. The problems introduced by this approach are twofold. First the centralization of these functions becomes untenable as the size of the network increases.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.26-38
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• 2016

Recently, mobile traffic has increased tremendously due to the deployment of smart devices such as smartphones and smart tablets. Android is the world's most powerful mobile platform in smartphone. The Android operating system provide seamless access to many applications and access to the Internet. It would involve network packet sharing communicated over the network. Network packet contains a lot of useful information about network activity that can be used as a description of the general network behaviours. To study what is the behaviours of the network packet, an effective tools such as network packet analyzers software used by network administrators to capture and analyze the network information. In this research, more understanding about network information in live network packet captured from Android smartphone is the target and identify the best network analyzer software.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.202-205
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• 2005

Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.360-363
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• 2018

Human activity is influenced by various factors, from individual physical features such as vertebral flexion and pelvic distortion to feelings such as joy, anger, and sadness. However, the nature of these behaviors changes over time, and behavioral characteristics do not change much in the short term. The activity data of a person has a time series characteristic that changes with time and a certain regularity for each action. In this study, we applied LSTM, a kind of cyclic neural network to deal with time - series characteristics, to the technique of recognizing activity type and improved recognition rate of activity type by measuring time and parameter optimization of components of LSTM model.

• Journal of Korean Society for Quality Management
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• v.44 no.1
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• pp.199-214
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• 2016

Purpose: The present study was designed to examine the casual relationships among performance and arts information service quality, initial trust, user satisfaction, reuse intention and prosumer activity in social network service(SNS). Also, we intended to explore significant factors on use performance of SNS through causal model analysis in the viewpoint of total effect. Methods: As a survey tool, questionnaire has obtained validity and reliability through literature survey, exploratory survey and pretest and sample 403 was selected. For statistical treatment of pretest and main analysis, SPSS18.0 and AMOS18.0 were employed and structural equation model was employed as analysis method. Results: Result of this study shows as follows. Two factors (precision and reciprocal action) have an effect on user satisfaction, initial trust, reuse intention and prosumer activity. We found that with an importance of initial trust, prosumer activity can be a useful and significant factor in causal relationship of SNS. Conclusion: The present study shows that two factors(precision and reciprocal action) in via of initial trust, were important factors that related companies have to emphasize to raise performance, And also we confirmed new factor 'prosumer activity' through this study. However, the present study has some limitations to be studied in the future.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.137-144
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• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.

• Journal of information and communication convergence engineering
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• v.5 no.4
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• pp.382-388
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• 2007

Indoor location system using wireless sensor network technology was applied for the status evaluation and activity monitoring of elderly person or chronic invalid at home. Location awareness application is transparent to the daily activities, while providing the embedded computing infrastructure with an awareness of what is happening in this space. To locate an object, the active ceiling-mounted reference beacons were placed throughout the building. Reference beacons periodically publish location information on RF and ultrasonic signals to allow application running on mobile or static nodes to study and determine their physical location. Once object-carried passive listener receives the information, it subsequently determines it's location from reference beacons. By using only the sensor nodes without any external network infrastructure the cost of the system was reduced while the accuracy in our experiments. was fairly good and fine grained between 7 and 15 cm for location awareness in indoor environments. Passive architecture used here provides the security of the user privacy while at the server the privacy was secured by providing the authentication using Geopriv approach. This information from sensor nodes is further forwarded to base station where further computation is performed to determine the current position of object and several applications are enabled for context awareness.