In this paper, we propose a gesture recognition system to recognize motions using the shape information of MHI (Motion History Image). The system acquires MHI to provide information on motions from images with input and extracts the gradient images from such MHI for each X and Y coordinate. It extracts the shape information by applying the shape context to each gradient image and uses the extracted pattern information values as the feature values. It recognizes motions by learning and classifying the obtained feature values with a SVM (Support Vector Machine) classifier. The suggested system is able to recognize the motions for multiple people as well as to recognize the direction of movements by using the shape information of MHI. In addition, it shows a high ratio of recognition with a simple method to extract features.
Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction while maintaining most of the variation in data. Although PCA has been applied in many areas successfully, it is sensitive to outliers and only valid for Gaussian distributions. Several variants of PCA have been proposed to resolve noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA, however, is still a linear algorithm that cannot accommodate non-Gaussian distributions. In this paper, a non-linear algorithm that combines RF-PCA2 and kernel PCA (K-PCA), called improved robust kernel fuzzy PCA (RKF-PCA2), is introduced. The kernel methods make it to accommodate non-Gaussian distributions. RKF-PCA2 inherits noise robustness from RF-PCA2 and non-linearity from K-PCA. RKF-PCA2 outperforms previous methods in handling non-Gaussian distributions in a noise robust way. Experimental results also support this.
Ultrasound images for the abdominal muscles are complicated enough to have difficulty in interpreting their results. For better interpretation, magnifying the original image is necessary but its magnified image could be deteriorated and suffer from information loss. Thus, in this paper, we propose a magnifying method that reduces the gap between the original image and the magnified one in quality using a fuzzy method with weights for its brightness and interpolation. The proposed method extracts information of pixels in magnified image that have most similar characteristics of the original one by applying fuzzy membership function. In the process, the difference in the brightness between pixels of the magnified image and the original one using bilinear interpolation method and the weight value using the interpolation from multiplied values of four pixels are supplied to the fuzzy membership function. In this experiment, the proposed method reduces the cloudy phenomenon appears commonly compared to the bilinear interpolation method among those qualitative issues of image interpretation.
This study deals with the assessment indices about greening performance of datacenter. To do this, we survey existing standards and guidelines about datacenter. Those are used constructing new model of datacenter regarding greening performance. In this model, the relationship between major components will be represented by job-energy-thermal notation. Items, criteria and methods are created in order to complete the assessment indices. The existing assessment indices of the datacenter level and equipment level are investigated. Parameters, the degree of virtualization and energy efficiency also affects the performance of the green performance. Finally, to determine the improvements, the proposed indices is compared with the existing assessment tools.
With the rapid development of technology, skyscrapers are widely spread and they are tightly coupled. If fire occurs in a building, it is easily spread to neighboring buildings, resulting in the large number of victims and property damages. To remove fire disasters, the need for early fire detection techniques is increasing. To detect fire, detecting devices for heat, smoke, and flame have been used widely. However, this paper surveys and presents the latest research which focuses on early smoke and flame detection algorithms and systems with camera’s input images. In addition, this paper implements and evaluates the performance of these flame and smoke detection algorithms with several types of movies.
This paper proposes fusion methods of license plate detection and super-resolution for improving license plate recognition in low-resolution images. In the proposed method, we apply the license plate detection based on local structure pattern feature and the sequential super-resolution based on Kalman filter. The proposed fusion methods are divided into two according to whether the license plate is detected or not in the input image : (i) performing license plate detection after restoring whole image through super resolution, and (ii) restoring only the detected region through super-resolution after detecting the license plate. We demonstrated effectiveness of the proposed methods in various environments.
In this paper, we proposed the method for detecting text region on image using DCT-coefficient and transition-map analysis. The detecting rate of traditional method for detecting text region using DCT-coefficient analysis is high, but false positive detecting rate also is high and the method using transition-map often reject true text region in step of verification because of sticky threshold. To overcome these problems, we generated PTRmap(Promising Text Region map) through DCT-coefficient analysis and applied PTRmap to method for detecting text region using transition map. As the result, the false positive detecting rate decreased as compared with the method using DCT-coefficient analysis, and the detecting rate increased as compared with the method using transition map.
In this paper, an improved block-based background modeling technique using adaptive parameter estimation that judiciously adjusts the number of model histograms at each frame sequence is proposed. The conventional block-based background modeling method has a fixed number of background model histograms, resulting to false negatives when the image sequence has either rapid illumination changes or swiftly moving objects, and to false positives with motionless objects. In addition, the number of optimal model histogram that changes each type of input image must have found manually. We demonstrate the proposed method is promising through representative performance evaluations including the background modeling in an elevator environment that may have situations with rapid illumination changes, moving objects, and motionless objects.
The purpose of this thesis was to implement effective blended PBL(Problem-Based Learning) systems for information communication ethics education. Online learning and face-to-face classes were systematically combined for achieving the teaching-learning goals. And the main module for online learning run on Moodle, an open source learning management system. To examine educational effectiveness of the proposed systems, an experimental study was conducted through the method to the subject of two class in the second-grade of university located in OO city. For experiment 60 students(treatment group=30, control group=30) are participated. And they were assigned to one of ten subgroups, comprising of six students, respectively. The results of this study are follows, First, the education using proposed blended PBL method is more effective in cultivating consciousness of information communication ethics than the education using face-to-face PBL learning method. Second, learners who participated in the proposed blended PBL more experienced various effects of PBL, such as (1) Improvement of problem solving ability, (2) Understanding of cooperative learning than the other learners who participated in the face-to-face PBL.
Recently, the development of internet technology makes user's personal data used by being saved in USB. But there is a critical issue that personal data can be exposed with malicious purpose because that personal data doesn't need to be certificate to use. This paper proposes USB security framework to prevent a duplicate use of personal data for protecting the data which in USB. The proposed USB security framework performs certification process of user with additional 4bite of user's identification data and usage choice of USB security token before certification data when the framework uses USB security product in different network. It makes communication overhead and service delay increased. As a result of the experiment, packet certification delay time is more increased by average 7.6% in the proposed USB security framework than simple USB driver and USB Token, and procedure rate of certification server on the number of USB is also increased by average 9.8%.
A smart grid networks delivers electricity from suppliers to consumers using digital technology with two-way communications to control appliances at consumers' homes to save energy, reduce cost and increase reliability and transparency. Security is critically important for smart grid networks that are usually used for the electric power network and IT environments that are opened to attacks, such as, eavesdroping, replay attacks of abnormal messages, forgery of the messages to name a few. ZigBee has emerged as a strong contender for smart grid networks. ZigBee is used for low data rate and low power wireless network applications. To deploy smart grid networks, the collected information requires protection from an adversary over the network in many cases. The security mechanism should be provided for collecting the information over the network. However, the ZigBee protocol has some security weaknesses. In this paper, these weaknesses are discussed and a method to improve security aspect of the ZigBee protocol is presented along with a comparison of the message complexity of the proposed security protocol with that of the current ZigBee protocol.
In this paper, we propose an QoS aware multi-layer MAC(QAML-MAC) protocol in a wireless sensor networks. Since the proposed protocol is based on the sleep-awake architecture, which save node's energy to prolong the entire network lifetime. For this purpose the QAML-MAC first classifies incoming data according to their transmission urgency and then saves them. The protocol also adapts the cross-layer concept to re-arrange the order of transmission with the same destination. So the delay can be decreased, which can not be obtained with the previous related protocols. And high priority data such as real-time multimedia or critical value in the field monitoring applications can be transmitted quickly, Furthermore the proposed protocol has advantage of decreasing transmitted data collisions using multiple layers of idle listening when there is no high-priority data. So energy consumptions of sensor nodes can be saved and the network lifetime can be prolonged.
When ZigBee network supports beacon transmission mechanism, ZigBee devices have the restriction of the transmission range from the coordinator. On the contrary, when it does not support beacon transmission, it is not easy to save the energy through turning into sleep mode. This paper proposes active slot allocation method that allocates a transmission time and does not use the beacon transmission mechanism. It is based on the ZigBee's distributed address assignment mechanism and supports the scalability. This paper explains the active slot structure and the allocation order and describes the operation of ZigBee devices. We verify the proposed mechanism through the simulation and show the performance evaluation. It can be useful on the industrial automation and the environmental surveillance.
Efficient ordering of decision variables is one of the methods that find solutions quickly in the depth first search using backtracking. At this time, development of variables ordering algorithms considering dynamic and static properties of the problems is very important. However, to exploit optimal variable ordering algorithms appropriate to the problems. In this paper, we propose a problem classifying rule which provides problem type based on variables' properties, and use this rule to predict optimal type of variable ordering algorithms. We choose frequency allocation problem as a DS-type whose decision variables have dynamic and static properties, and estimate optimal variable ordering algorithm. We also show the usefulness of problem classifying rule by applying base station problem as a special case whose problem type is not generated from the presented rule.
In the past, the domestic research on mobile forensics has been limited to cell phones. Increasing use of smart phones, studies on smart phone forensic will be conducted actively in the future. In particular, the study on Android forensic is very important because Android smart phone market share is increasing rapidly. In this paper, we describe an implementation of an Android smart phone forensic tool based on logical analysis. Compared with Oxygen Forensic Suite 2010, this tool saves time it takes to perform Android smart phone forensic because this tool provides search feature and resource links for extracted media information. So far, no smart phone forensic tool is introduced in Korea. Accordingly, this tool would contribute to the advancement of the technology on smart phone forensic.
Passive tags are inferior to active tags in processing efficiency, so they have difficulty in large‐volume processing. The proposed protocol reduces the volume of computation in passive tags and, at the same time, improves authentication for enhanced safety and security. That is, different from existing RFID protocols that return the same value even if an error happens when the reader reads a tag, the improved RFID security protocol returns a new value using a re-counter and processes the computation part of a tag in the reader or in a back‐end system. Even if the information of a tag is acquired by an malicious way, it is not actual information but encrypted information that is not usable. In addition, even if tag information is read in sequence, it is changed in each read, so the protocol is safe from Location Tracking.
As e-commerce and social media service evolves, studies on recommender systems advance, especially concerning the application of collective intelligence to personalized custom service. With the development of smartphones and mobile environment, studies on customized service are accelerated despite physical limitations of mobile devices. A typical example is combined with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A profile of movie genre similarity is generated and designed to provide related service in mobile experimental environment before prototyping and testing with data from MovieLens.
As demand for efficiency in handling dynamic XML data grows, new dynamic XML labeling schemes have been researched. The key idea of the dynamic XML labeling scheme is to find ancestor-descendent-sibling relationships and to minimize memory space to store total label, response time and range of relabeling incurred by update operations. The prime number labeling scheme is a representative scheme which supports dynamic XML documents. It determines the ancestor-descendant relationships between two elements by a simple divisibility test of labels. When a new element is inserted into the XML data using this scheme, it does not change the label values of existing nodes. However, since each prime number must be used exclusively, labels can become significantly large. Therefore, in this paper, we introduce a novel technique to effectively reduce the problem of label overflow. The suggested idea is based on tree decomposition. When label overflow occurs, the full tree is divided into several sub-trees, and nodes in each sub-tree are separately labeled. Through experiments, we show the effectiveness of our scheme.
Multiple roadmap DB providers are already available in these days, and try to reduce unknown roads in their own roadmaps. However, cooperation models or Win-Win approaches between roadmap providers are not considered yet. Thus, In this paper, We proposed a cloud-oriented real-time roadmap generation and update method between heterogeneous navigation systems for unknown roads. With the proposed method, the roadmap DB providers update the own roadmap DB for navigation systems in real time. Also, they can provide the complete roadmap without unknown roads to users instantly. Therefore, the proposed method can reduce the costs of an actual traveling test and the maintenance for the roadmap DB provides. Thus, the cloud-oriented roadmap generation method can more efficiently update the unknown road information.
The main issues of the researches are monitoring environment such as weather or temperature variation and natural accident, and sensor gateways which have mobile device, applications for mobile health care. In this paper, we propose the SFMS(Smart Factory Management System) that can effectively monitor and manage a green smart factory area based on M2M service and smart phone with android OS platform. The proposed system is performed based on the TinyOS-based IEEE 802.15.4 protocol stack. To validate system functionality, we built sensor network environments where were equipped with four application sensors such as Temp/Hum, PIR, door, and camera sensor. We also built and tested the SFMS system to provide a novel model for event detection systems with smart phone.
In this research, we have developed a virtual interview simulation system that utilizes the 3D stereoscopic technology. For this virtual simulation can play individual question stereoscopic movies using seamless loop technology, it provides realistic environment and interviewee with 3D filmed interviewer increasing reliable experience for interviewees. Implementing question-pool system and real-time construction of questionnaires is also available so that the interviewee can train and prepare for the various situations. This will reduce the effort for work power, time, place and cost, opening for a possibility of utilizing for many other areas such as linguistics study and public sector.
With the success of Kindle, an electronic book reader developed by Amazon.com, there has been a growing interest in both electronic books and readers in Korea. In this paper, we analyze electronic book user needs through fuzzy analytic hierarchy process (AHP) and conjoint analysis. First, we select the important factors which can affect the intention to purchase electronic book readers by applying the fuzzy AHP with the help of electronic book experts. Next, we perform conjoint analysis to reveal the detailed needs of electronic book users for each of the selected factors. Some useful implications and research limitations are also presented with future research directions.
It is difficult to apply a Software Process Certification to small scale project, because of much activities and documents for manage project and guarantee the qualities in small manpower. This paper presents optimized Software Process Certification Model for small scale software development, a combine Scrum with essential elements of NIPA's(National IT industry Promotion Agency) SP-Certification model. The proposed model defined minimum Activities and Documents for SP Certification. The model that I showed consists of 16 Processes and 58 Activities, and 39 Artifacts are created. As a result of having compared proposed model with a Standard Process of Small Business, I confirmed that a small scale project's burden reduced because Activity decreased 38% and Document decreased 20%. In order to verify the validity of the proposed model, applied it in two small scale projects, and compare with the project by Scrum process only, it finished that systematic management was possible without additional manpower, and reached SP-Certification level 2.
In this paper, we propose a remote control station based on the awareness of various combat field situation in order for operating multiple unmanned ground vehicles. Our remote control station is capable of sending a variety of messages designed for carrying out the skillful movement for collaborate among unmanned ground vehicles, gathering the information related with combat field situation, and completing the assigned missions which are described by operator in advance. To verify the effectiveness of our proposal, we develop the sophisticated remote control station and conduct a great many remote operating tests for multiple unmanned ground vehicles.
A large number of industry and trade circulation enterprises integrate logistics resource. They give links of product transport to some professional logistics enterprises in order to reduce costs. We call these professional logistics enterprises as the Third-party Logistics. As the development of the computer and internet, the suppliers, buyers and the Third-party Enterprises connect each other with internet. And different company use different management software, so heterogeneous data become a big problem of the information system for Third-party Enterprises. We built the logistics ontology with protégé, and translate it in OWL. We also built the rules for Logistics Ontology to improve the limitations of the OWL. Then we design the intelligent system for 3PL Enterprises Distribution Center based on Logistics Ontology and Logistics Rules. At final, we give an example to show the workflow visually.
The study was to test hypothesis that social support and family function affect on life satisfaction, suggested the policy implications. I established research model based on reference review and took a questionnaire on the life satisfaction to the middle-aged and old-aged people over the age of fifty, who live in G city among the attendants at the program that was held by the local government. According to analytical results, social support and family function affected on social participation and life satisfaction. This study suggested theoretical and policy implications based on analytical results.