• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.73-86
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• 2012

This paper theoretically derives an algorithm to discover a new type of social networks from workflow models, which is termed workflow-based social network intelligence. In general, workflow intelligence (or business process intelligence) technology consists of four types of techniques that discover, analyze, monitor and control, and predict from workflow models and their execution histories. So, this paper proposes an algorithm, which is termed ICN-based workflow-based social network intelligence discovery algorithm, to be classified into the type of discovery techniques, which are able to discover workflow-based social network intelligence that are formed among workflow performers through a series of workflow models and their executions, In order particularly to prove the correctness and feasibility of the proposed algorithm, this paper tries to apply the algorithm to a specific workflow model and to show that it is able to generate its corresponding workflow-based social network intelligence.

• Smart Media Journal
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• v.11 no.5
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• International Journal of Contents
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• v.8 no.3
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• pp.1-6
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• 2012

This study establishes a simple Causal Map in order to understand the structure of Social Network Services(SNS). From this, the paper proposes a simulation with drawing Simulation Model and it examines whether a Dunbar Number is available for the emergence of collective intelligence based on an on-line network. Through my analytical research, it turn out both Closed SNS and Open SNS have their own Dunbar Number. However, it appear that Open SNS can expand infinitely as it has a unique property namely weak tie. This implies that it is significant for a system or policy to be developed in order to overcome the problem of Dunbar Number, whereas restricting the extension of a network by considering the Dunbar Number would have a negative impact on emergence of collective intelligence.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.13-26
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• 2023

Recently, the development of wireless network technologies has been advancing in several directions: increasing data transmission speed, enhancing user mobility, expanding the range of services offered, improving the utilization of the radio frequency spectrum, and enhancing the intelligence of network and subscriber equipment. In this research, a series of contradictions has emerged in the field of wireless network technologies, with the most acute being the contradiction between the growing demand for wireless communication services (on operational frequencies) and natural limitations of frequency resources, in addition to the contradiction between the expansions of the spectrum of services offered by wireless networks, increased quality requirements, and the use of traditional (outdated) management technologies. One effective method for resolving these contradictions is the application of artificial intelligence elements in wireless telecommunication systems. Thus, the development of technologies for building intelligent (cognitive) radio and cognitive wireless networks is a technological imperative of our time. The functions of artificial intelligence in prospective wireless systems and networks can be implemented in various ways. One of the modern approaches to implementing artificial intelligence functions in cognitive wireless network systems is the application of fuzzy logic and fuzzy processors. In this regard, the work focused on exploring the application of fuzzy logic in prospective cognitive wireless systems is considered relevant.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.15-21
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• 2004

A number of Humanoids are introduced including ASIMO, HRP-2 Promet, Johnniee, Babybot, and KHR-2. Most researches are focused on the development of stable biped walking of Humanoids and it is not easy to endow an Humanoid with intelligence and service technology until now in the sense that the operation time of a Humanoid is limited less than 30 minutes even in the case that the battery is used only for the control of actuators in a Humanoid. In this paper, a brief survey on Humanoids is proposed and the concept of 'Network-based Humanoid', a Humanoid being able to provide intelligence for human-friendly services using ubiquitous networks, is introduced briefly.

• ETRI Journal
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• v.41 no.6
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• pp.760-770
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• 2019

Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.92-100
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• 2019

Vast amounts of different service-specific requirements and vertical network slicing in a 5G network increase the complexity, cost of the network management and resource operations for carriers. To solve this problem, 3GPP is working on the standardization of NWDAF to support the automation of the 5G network by utilizing artificial intelligence technologies based on Big Data to improve the efficiency of network management and resource operation. In addition, the ETSI ZSM Industry Specification Group is developing technical standards for the automation of end-to-end network management and service delivery. This document provides an overall survey of the latest standardization issues of the NWDAF in 3GPP and ETSI ZSM for 5G network automation and intelligence.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.109-118
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• 2012

This paper theoretically derives an algorithm to discover a new type of workflow-based knowledge from workflow models, which is termed workflow-based affiliation network knowledge. In general, workflow intelligence (or business process intelligence) technology consists of four types of techniques that discover, analyze, monitor and control, and predict a series of workflow-based knowledge from workflow models and their execution histories. So, this paper proposes a knowledge discovery algorithm which is able to discover workflow-based affiliation networks that represent the association and participation relationships between activities and performers defined in ICN-based workflow models. In order particularly to prove the correctness and feasibility of the proposed algorithm, this paper tries to apply the algorithm to a specific workflow model and to show that it is able to derive its corresponding workflow-based affiliation network knowledge.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.174-179
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• 2012

The social and economic level of damages might be highly increased in the case of wide-area black-outages, because of heavy dependence of electricity. Therefore, the development of a wide-area protection relay intelligence techniques is required to prevent massive power outages and minimize the impact strength at failure. The frequency monitoring and prediction for wide-area protection relaying intelligence has been considered as an important technology. In this paper, a network-based frequency monitoring system developed for wide-area protection relay intelligence is presented. In addition, conventional techniques for frequency estimation are compared, and a method for advanced frequency estimation and measurement to improve the precision is proposed. Finally, an integrated monitoring system called K-FNET(Korea-Frequency Monitoring Network) is implemented based on the GPS and various energy monitoring cases are studied.