• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.842-846
• /
• 1993

Because of complexity, neural network is difficult to learn. So if internal representation[1] can be performed successfully, it is possible to use perceptron learning rule. As a result, learning is easier. Therefore the method of internal representations applied to the "XOR" problem, and the "spirals" problem. And then using the above results, the structure of neural network for computing is embodied.mputing is embodied.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.17 no.5
• /
• pp.17-30
• /
• 2022

It has been verified in several studies that dynamic capabilities has a very important effect on the competitive advantage of technology startups. And the network has an important influence on this dynamic capability. This is even more important for start-ups that lack the resources and knowledge. Networks that directly and significantly affect dynamic capabilities have been studied mainly the value chain internal. However, network activities of start-ups are conducted not only with the value chain internal networks but also with the value chain external networks. Therefore, it is necessary to study the effect of the value chain internal and external network activity of start-ups on the dynamic capabilities, but prior studies are lacked. In this study, We make a model that encompass the value chain internal and external network for technology startups, and a study was conducted to demonstrate the effect on dynamic capabilities and competitive advantage. As a result of the study, value chain internal network activity directly and significantly affected dynamic capabilities, and value chain external network activity did not directly significantly affect dynamic capacity. And dynamic capabilities had a significant effect on competitive advantage. As confirmed through additional research, value chain external network activity affects value chain internal network activity, and through this, dynamic capabilities are strengthened, and positively affect competitive advantage.. The intensity of value chain external network activity was not significant to dynamic capabilities and the diversity of value chain external network activity had a significant effect on the competitive advantage by double mediating the value chain internal network activity and dynamic capability. Through this study, it is confirmed that the value chain internal networks is important in order for startups to strengthen their dynamic capabilities and increase their competitive advantage, and that both strong and diversified the value chain internal networks positively affects competitive advantage by enhancing dynamic capabilities.

• Asia pacific journal of information systems
• /
• v.26 no.2
• /
• pp.299-321
• /
• 2016

Previous studies on strategic alliance formation have largely overlooked the effects that organizations' routine development can have on the relationship between organizations' network position and their alliance ambidexterity strategy. This study extends ambidexterity research by adding internal and network perspectives and examining their cumulative effects on alliance ambidexterity. We first acknowledge the interplay between organizations' internal knowledge exploration/exploitation strategies and organizations' alliance ambidexterity and determine that organizations with a high level of internal knowledge breadth are more likely to make focused alliance decisions. Second, our analysis of 145 US-based information technology organizations with an active alliance behavior reveals that having well-formed routines as a result of previous collaborations strengthens the tendency of brokerage organizations to follow alliances that focus on either exploration or exploitation. Although most alliance studies have commonly argued in favor of an ambidextrous approach, this study provides critical evidence that both internal knowledge exploration/exploitation strategies and development of routines constrain organizations' alliance formation decisions, guiding them towards a more focused approach.

• Herbal Formula Science
• /
• v.30 no.3
• /
• pp.145-154
• /
• 2022

Objective : This study aims to study core herbs and formulas in Internal medicine on Spleen system, to enhance efficiency in teaching Internal medicine on Spleen system, Herbalogy, Formula science, and to increase integration of the courses. Methods : Frequency notion, which was generally used in previous studies, was used in this study along with network analysis. Results : Frequently used herbs, herbs with high centrality, frequently combined herbs and core formula were found in this study. The herb with the highest frequency and centrality was 'Citri Unshius Pericarpium', and 'Atractylodis Rhizoma Alba - Citri Unshius Pericarpium' was the most frequent herb combination. The results of network analysis showed a total of 5 herbal communities of combination. Conclusion : Core herbs were found based on the frequency notion, which is a traditional analysis method. Also, core herbs, herbal combinations, formulas that can that may be overlooked when using frequency notions were found by using network analysis. The results may lead to enhancing efficiency in the education of Internal medicine on Spleen system, Herbalogy, Formula science courses and the integration of courses.

• Smart Structures and Systems
• /
• v.8 no.1
• /
• pp.69-92
• /
• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.265-268
• /
• 1998

This paper evaluates the VSN(Virtual switch Network) characteristics in the internal call processing of IMT-2000 switching system, which is composed of VSN instead of ATM switch network. In results, internal call establishment delay is increased approximately 5.4msec than the conventional ATM switching system. The evaluated condition is the load 0.8, and the 100km distance between VSNs. It is confirmed that the VSN has the potentiality in the practical implementation.

• Journal of Digital Convergence
• /
• v.10 no.6
• /
• pp.341-347
• /
• 2012

Network Access Control system of medical asset protection solutions that installation and operation on system and network to provide a process that to access internal network after verifying the safety of information communication devices. However, there are still the internal medical-data leakage threats due to spoof of authorized devices and unauthorized using of users are away hours. In this paper, Network 2-Factor Access Control Model proposed for prevention the medical-data leakage by improving the current Network Access Control system. The proposed Network 2-Factor Access Control Model allowed to access the internal network only actual users located in specific place within the organization and used authorized devices. Therefore, the proposed model to provide a safety medical asset environment that protecting medical-data by blocking unauthorized access to the internal network and unnecessary internet access of authorized users and devices.

• Convergence Security Journal
• /
• v.19 no.3
• /
• pp.51-59
• /
• 2019

In this paper, we propose a device identification system for network visibility that can maintain the secure internal network environment in the IoT era. Recently, the area of enterprise network is getting huge and more complicated. Not only desktops and smartphones but also business pads, barcode scanners, APs, Video Surveillance, digital doors, security devices, and lots of Internet of Things (IoT) devices are rapidly pouring into the business network, and there are highly risk of security threats. Therefore, in this paper, we propose the device identification system that includes the process and module-specific functions to identify the exploding device in the IoT era. The proposed system provides in-depth visibility of the devices and their own vulnerabilities to the IT manager in company. These information help to mitigate the risk of the potential cyber security threats in the internal network and offer the unified security management against the business risks.

• Convergence Security Journal
• /
• v.12 no.3
• /
• pp.53-58
• /
• 2012

Network Access Control System that is one of the efforts to protect the information of internal applies to effectively control of insider and automatic network management and security. However, it has some problems : spoofing the authorized PC or mobile devices, connect to the internal network using a system that authorized users are away. In addition, information leakage due to malicious code in the same system. So in this paper, Network 2-Factor Access Control System based on RFID security control system is proposed for safety communication environment that performing a two-factor authentication using authorized user and devices to connect to the internal network.

• Structural Monitoring and Maintenance
• /
• v.3 no.3
• /
• pp.297-314
• /
• 2016

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (${\theta}$) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.