• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1576-1583
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• 1989

In this paper, we describe real-time optical implementation of the Hopfield neural network model for two-dimensional associative memory by using commercial LCTV and Multifocus For real-time processing capability, we use LCTV as a memory mask and a input spatial light modulator. Inner product between input pattern and memory matrix is processed by the multifocus holographic lens. The output signal is then electrically thresholded fed back to the system input by 2-D CCD camera. From the good experimental results, the proposed system can be applied to pattern recognition and machine vision in future.

• Spatial Information Research
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• v.5 no.1
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• pp.11-26
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• 1997

The purpose of this study is to analyze the locational characteristics of bank branch offices in Kangnam-Gu, Seoul by using Geographic Information System. The number of bank branch offices have sharply increased due to financial liberalization, while the scale of them is getting smaller. The procedure of this research has four steps. First, the spatial distribution of bank branch offices in Seoul is analyzed by the places and time. Second, the spatial variations of bank offices in dong districts of Seoul is explained by factor analysis and multiple regression analysis. Third, the location-allocation model which is embedded within network module in Arc/Info is applied in order to find out optimal location of bank offices in Kangnam-Gu. Finally, the grid module is used in creating the potential surface map for locational sites of new bank branch offices The factors to affect the location of the bank offices contain mainly economic variables including local tax, collUl1ercial area, total establismnent and total employment. The actual locational pattern of bank offices is similar to the idealized locational pattern proposed by the function of min-distance in location-allocation models. In conclusion, this study shows that spatial analysis functions may potentially be improved using GIS technologies. However in order to analyze the location of bank offices more precisely, it should be found out the way to collect more appropriate data, construct computerized base maps, and investigate consumer behaviour and behavioural characteristics of bank themselves..

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.189-196
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• 2010

Spatial information and smart phone technology have made innovative improvement of daily life. Spatial and geographic information are in practice for various applications. Especially, spatial information along with information and telecommunication technology could create new contents for providing services for convenient daily life. Spatial information technology, recently, is not only for acquiring location and attribute data but also providing tools to extract information and knowledge systematically for decision making. Various indoor applications have emerged in accordance with demands on daily GIS(Geographic information system). This paper aims for applying spatial information technology to support decision-making in shopping. The main contents include product database, optimal path search, shopping time expectation, automatic housekeeping book generation and analysis. Especially for foods, function to analyze information of the nutrition facts could help to improve dietary pattern and well-being. In addition, this system is expected to provide information for preventing overconsumption and impulse purchase could help economical and effective purchase pattern by analyzing propensity to consume.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.40-46
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• 2000

In this paper, a transient identification based on a Hidden Markov Model (HMM) has been suggested and evaluated experimentally for the classification of transients in the dynamic process. The transient can be identified by its unique time dependent patterns related to the principal variables. The HMM, a double stochastic process, can be applied to transient identification which is a spatial and temporal classification problem under a statistical pattern recognition framework. The HMM is created for each transient from a set of training data by the maximum-likelihood estimation method. The transient identification is determined by calculating which model has the highest probability for the given test data. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. Several experimental tests have been performed including superimposing random noise, adding systematic error, and untrained transients. The proposed real-time transient identification system has many advantages, however, there are still a lot of problems that should be solved to apply to a real dynamic process. Further efforts are being made to improve the system performance and robustness to demonstrate reliability and accuracy to the required level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.807-824
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• 2016

In this paper, a method of mood classification based on user brainwaves is proposed for real-time application in commercial services. Unlike conventional mood analyzing systems, the proposed method focuses on classifying real-time user moods by analyzing the user's brainwaves. Applying brainwave-related research in commercial services requires two elements - robust performance and comfortable fit of. This paper proposes a filter based on Regularized Common Spatial Patterns (RCSP) and presents its use in the implementation of mood classification for a music service via a wireless consumer electroencephalography (EEG) device that has only 14 pins. Despite the use of fewer pins, the proposed system demonstrates approximately 10% point higher accuracy in mood classification, using the same dataset, compared to one of the best EEG-based mood-classification systems using a skullcap with 32 pins (EU FP7 PetaMedia project). This paper confirms the commercial viability of brainwave-based mood-classification technology. To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated. Furthermore, as a prototype service, this paper introduces a mood-based music list management system called MyMusicShuffler based on the proposed mood-classification method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• The Korean Journal of Ecology
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• v.16 no.3
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• pp.317-327
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• 1993

Stands profiles, yearly changes in growth of annual rings, age and diameter structure, and spatial distribution pattern of individuals in the Pinus densiflora stands around the Yeocheon industrial complex were investigated. Growth of annual ring in Pinus densiflora, which survived when vegetation of this area was damaged by air pollutants, was suppressed for about 10 years since 1974 when factories in this area began to operate, but since then such suppressed growth tended to be recovered. It was supposed that the suppresed growth was originated from air pollution and that improvement of growth since the suppressed period was due to the release from competition with them by death of neighbouring trees and the resuction of the amount of air pollutants. Physiognomy of Pinus densiflora stands showed mosaic pattern composed of different patches. Spatial distribution pattern of individuals an stand profiles were similar to those of Pinus densiflora stands regenerated after natural and artificial disturbances. In an age distribution diagram, age of Pinus densiflora population ranged from 1 to 33 years, Among these individuals were recrited corresponded to the suppresed period of growth of annual ring in Pinus densiflora survived when the vegetation was damaged by air pollution. On the other hand, from the result of analysis of frequency distribution diagram of diameter, it was postulated that even if whis Pinus densiflora community can be maintained as it is for the time being, it might be changed to Quercus community with the lapse of time. Regeneration; Pinus densiflora; Air pollution; Annual ring; Age structure; Diameter structure; Quercus spp. In these analyses, factors for individual recognition and species recognition were suggested.

• Ecology and Resilient Infrastructure
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• v.9 no.2
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• pp.83-92
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• 2022

The 2020 summer extreme flood severely disturbed the riparian ecosystem of the Seomjin River. Some trees were killed by the flood impact, whereas others have recovered through epicormic regeneration after the disturbance. At the same time, several tree individuals newly germinated. This research aimed to explain the recovery of the riparian ecosystem by spatial proximity between each tree individual of different characteristics, such as "dead", "recovered", and "newly germinated". A spatial point pattern analysis based on K and g-functions revealed that the newly germinated trees and the existing trees were distributed in the spatially clumping patterns. However, further detailed analysis revealed that the new trees were statistically less attracted to the recovered trees than the dead trees, implying competitive interactions hidden in the facilitative interactions. Habitat amelioration by the existing trees positively affected the growth of the new trees, while "living" existing trees were competing with the new trees for resources. This research is expected to provide new knowledge in this era of rapid climate change, which likely induces stronger and more frequent natural disturbance than before. Environmental factors have been widely used for ecosystem modeling, but species interactions, represented by the relative spatial distribution of plant individuals, are also valuable factors explaining ecosystem dynamics.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1040-1043
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• 2001

This paper describes the principle behind Crank Angle Analysis, as implemented by Bruel ＆ Kjaer in the Non-Stationary Spatial Transformation of Sound Fields (NS-STSF) system. The NS-STSF system combines a Time Domain Holography measurement on for example an engine with two simultaneously recorded Tacho signals. The Tacho signals provide the crankshaft angle and the RPM at the instant of each instantaneous output (snap-shot) from Time Domain Holography. As a result, the system allows precise analysis of the temporal and spatial relation between the acoustical emission (or the vibration pattern) and the mechanical events during an engine cycle. Some results from a measurement on a DaimlerChrysler engine are presented.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.231-242
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• 2007

The existing pattern mining methods[1,2,3,4,5,6,11,12,13] do not use location generalization method on the set of location history data of moving object, but even so they simply do extract only frequent patterns which have no spatio-temporal constraint in moving patterns on specific space. Therefore, it is difficult for those methods to apply to frequent pattern mining which has spatio-temporal constraint such as optimal moving or scheduling paths among the specific points. And also, those methods are required more large memory space due to using pattern tree on memory for reducing repeated scan database. Therefore, more effective pattern mining technique is required for solving these problems. In this paper, in order to develop more effective pattern mining technique, we propose new location generalization method that converts data of detailed level into meaningful spatial information for reducing the processing time for pattern mining of a massive history data set of moving object and space saving. The proposed method can lead the efficient spatial moving pattern mining of moving object using by creating moving sequences through generalizing the location attributes of moving object into 2D spatial area based on $R^*$-Tree and Area Grid Hash Table(AGHT) in preprocessing stage of pattern mining.