• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1574-1582
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• 2004

Binary manipulators have recently received much attention due to hyper-redundancy, light weight, good controllability and high reliability. The precise positioning of the manipulator end-effecter requires the use of many modules, which results in a high-dimensional workspace. When the workspace dimension is large, existing inverse kinematics methods such as the Ebert-Uphoff algorithm may require impractically large memory size in determining the binary positions of all actuators. To overcome this limitation, we propose a new inverse kinematics algorithm: the inverse kinematics problem is formulated as an optimization problem using real-valued design variables, The key procedure in this approach is to transform the integer-variable optimization problem to a real-variable optimization problem and to push the real-valued design variables as closely as possible to the permissible binary values. Since the actual optimization is performed in real-valued design variables, the design sensitivity becomes readily available, and the optimization method becomes extremely efficient. Because the proposed formulation is quite general, other design considerations such as operation power minimization can be easily considered.

• Journal of Korean Neurosurgical Society
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• v.52 no.1
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• pp.67-70
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• 2012

We report a rare case of a spinal intramedullary ependymal cyst in a 46-year-old female and review the 17 pathologically proven cases in the literature. The patient presented with a two-week history of gradually increasing tingling in her left posterior thigh and calf. A preoperative magnetic resonance image revealed a well-defined intramedullary cystic lesion on the ventral side of the spinal cord at the T11 to T12 levels. The lesion was hyper intense in T2-weighted images and hypointense in T1-weighted. The patient underwent a right-side hemilaminectomy at the T11 to T12 levels and fenestration of the cyst wall. After having the cyst wall partially removed and communication established between the cyst and the subarachnoid space, the patient improved neurologically. A histological study of the surgical specimens revealed that the cyst wall consisted of glial cells lined by a simple cuboidal to columnar epithelium. An immunohistochemical examination of the cells lining the cyst wall was positive for S-100 protein, glial fibrillary acidic protein, epithelial membrane antigen, and cytokeratin. We suggest that the optimal treatment of intramedullary ependymal cysts creates adequate communication between the cyst and the subarachnoid space.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.105-109
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• 2020

The Future Army in 2050 should prepare for various future threats and effectively utilize its superintelligence and hyper-connected weapons systems to develop ways of fighting new concepts to dominate multi-regional battlefields and achieve victory. First, the establishment of active and offensive military strategies based on ability. Second, the battle of central strike for enemy combat will paralysis. Third, the battle of simultaneous integrated mosaic using multidisciplinary areas. Fourth, cyber warfare based on artificial intelligence that transcends time and space. Fifth, Combined Platform War. After all, future wars will be won or lost by invisible wars on cyber space.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.

• Korean Institute of Interior Design Journal
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• v.23 no.4
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• pp.63-71
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• 2014

Anamorphosis is highly favored in modern days as the atmosphere of pursuing unusual manners is growing while transformation and distortion of images are freely available. This research is to understand the affect of these distorted images on space designs and the close connection between anamorphosis and visual perceptions, and to identify the new perceptual phenomenon created through it, and the methods of expressing those. Four expressional methods were defined through the process of studying Anamorphosis based on its definition by Niceron, examining artworks such as paintings and photographs, and case-studying example spaces of visual perception experiments. Expressing anamorphosis through visual perceptions are broadly categorized to directional, dimensional, flatness, and optical. The analysis of 10 case projects suggests that the experimental spaces offer joys of finding and interpreting metaphorical forms and meanings caused by the four characteristic categories above. Also, they artificially show the boundaries between reality and virtual spaces in 2-dimensional or 3-dimensional spaces, and form hyper-boundaries, new experience, and an internal mechanism that is vague and chaotic. Therefore, this research concludes that anamorphosis which is a distorted perspective, is not only a simple measure to overcome perspectival errors, but is an existence suitable to the current era, that will extend its potential and value in spatial design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1005-1008
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• 1993

Supervised learnmg 01 recurrent neural networks (RNNs) is discussed. First, we review the present state of art, featuring their major properties in contrast of those of the multilayer neural networks. Then, we concisely describe one of the most practical learning algorithms, i.e. backpropagation through time. Revising the basic formulation of the learning algorithms, we derive a general formula to solve for the exact solution(s) of the whole connection weights w of RNNs. On this basis we introduce a novel interpretation of the supervised learning. Namely, we define a multidimensional Euclidean space, by assigning the cost function E(w) and every component of w to each coordinate axis. Since E=E(w) turns up as a hyper surface in this space, we refer to the surface as learning surface. We see that topological features of the learning surface are valleys and hills. Finally, after explicating that the numerical procedures of learning are equivalent to descending slopes of the learning surface along the steepest gradient, we show that a minimal value of E(w) is the intersection of curved valleys.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.439-448
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• 2006

Previous recognition/clustering algorithms such as Kohonen SOM(Self-Organizing Map), MLP(Multi-Layer Percecptron) and SVM(Support Vector Machine) might not adapt to unexpected input pattern. And it's recognition rate depends highly on the complexity of own training patterns. We could make up for and improve the weak points with lowering complexity of original problem without losing original characteristics. There are so many ways to lower complexity of the problem, and we chose a kernel concepts as an approach to do it. In this paper, using a kernel concepts, original data are mapped to hyper-dimension space which is near infinite dimension. Therefore, transferred data into the hyper-dimension are distributed spasely rather than originally distributed so as to guarantee the rate to be risen. Estimating ratio of recognition is based on a new similarity-probing and learning method that are proposed in this paper. Using CEDAR DB which data is written in cursive letters, 0 to 9, we compare a recognition/clustering performance of kSOM that is proposed in this paper with previous SOM.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.151-155
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• 2017

With the rapid growth of the up-to-date wireless network and Internet technologies, huge and various types of things around us are connected to the Internet and build the hyper-connected society, and lots of smart applications using these technologies are actively developed recently. IoT connects human, things, space, and data with various types of networks to construct the hyper-connected network that can create, collect, share and appling realtime information. Furthermore, most of the smart applications are concentrated on the service that can collect and store realtime contexts using various sensors and cloud technology, and provide intelligence by making inferences and decisions from them nowadays. In this paper, we design a smart application that can accurately control and process the current state of the specific context in realtime by using the state-of-the-art ICT techniques such as various sensors and cloud technologies on the IoT based mobile computing environment.

• Smart Media Journal
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• v.13 no.2
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• pp.156-164
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• 2024

The Metaverse as a combination of physical and digital space, provides many consumers with a different process of search, evaluation, consumption, and disposal than before, based on hyper-connected and hyper-realistic services. Therefore, it is necessary to examine what kind of sensory experiences customers feel when performing various activities in the Metaverse and how these experiences affect customers' behavioral responses. This study measured the temporal and emotional experience of the Metaverse service environment felt during the Metaverse experience journey of customers experiencing medical tourism services. The results of the study showed that the emotional experience through Metaverse had a more positive impact on customer satisfaction and customer loyalty as the intensity of the Metaverse experience deepened. In particular, it was confirmed that the emotional experience at the end of the Metaverse service had a positive influence on repurchase behavior. These results show that, just as in the real world, customer experience in the Metaverse can provide important insight into understanding customers' post purchase behavior and how service providers can develop and implement effective customer experience strategies in the Metaverse environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.281-288
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• 2024

The sixth generation(6G) wireless communication technology is advancing toward ultra-high speed, ultra-high bandwidth, and hyper-connectivity. With the development of communication technologies, the formation of a hyper-connected society is rapidly accelerating, expanding from the IoT(Internet of Things) to the IoE(Internet of Everything). However, at the same time, security threats targeting IoT devices have become widespread, and there are concerns about security incidents such as unauthorized access and information leakage. As a result, the need for security-enhancing solutions is increasing. In this paper, we implement an autoencoder-based anomaly detection model utilizing real-time collected network traffics in respond to IoT security threats. Considering the difficulty of capturing IoT device traffic data for each attack in real IoT environments, we use an unsupervised learning-based autoencoder and implement 6 different autoencoder models based on the use of noise in the training data and the dimensions of the latent space. By comparing the model performance through experiments, we provide a performance evaluation of the anomaly detection model for detecting abnormal network traffic.