This paper presents a method for computer-assisted slide presentation using vision-based gesture recognition. The proposed method consists of a sequence of steps, first detecting a hand in the scene of projector beam, then estimating the smooth trajectory of a hand or a pointing finger using Kalman Filter, and finally interfacing to an application system. Additional slide navigation control includes moving back and forth the pages of the presentation. The proposed method is to help speakers for an effective presentation with natural improved interaction with the computer. In particular, the proposed method of using finger pointing is believed to be more effective than using a laser pointer since the hand, the pointing or finger are more visible and thus can better grab the attention of the audience.

Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.

Classification methods based on dual energy X-ray absorptiometry, ultrasonic waves, and quantitative computed tomography have been proposed. Also, a classification method based on machine learning with bone mineral density and structural indicators extracted from the CT images has been proposed. We propose a method which enhances the performance of existing classification method based on bone mineral density and structural indicators by extending structural indicators and using principal component analysis. Experimental result shows that the proposed method in this paper improves the correctness of osteoporosis classification 2.8% with extended structural indicators only and 4.8% with both extended structural indicators and principal component analysis. In addition, this paper proposes a method of automatic phantom analysis needed to convert the CT values to BMD values. While existing method requires manual operation to mark the bone region within the phantom, the proposed method detects the bone region automatically by detecting circles in the CT image. The proposed method and the existing method gave the same conversion formula for converting CT value to bone mineral density.

To obtain good summarization algorithms, we need first understand how people summarize videos. 'Semantic gap' refers to the gap between semantics implied in video summarization algorithms and what people actually infer from watching videos. We hypothesized that ERP responses to real time videos will show either N400 effects to topic-irrelevant shots in the 300∼500ms time-range after stimulus on-set or P600 effects to topic-relevant shots in the 500∼700ms time range. We recruited 32 participants in the EEG experiment, asking them to focus on the topic of short videos and to memorize relevant shots to the topic of the video. After analysing real time videos based on the participants' rating information, we obtained the following t-test result, showing N400 effects on PF1, F7, F3, C3, Cz, T7, and FT7 positions on the left and central hemisphere, and P600 effects on PF1, C3, Cz, and FCz on the left and central hemisphere and C4, FC4, P8, and TP8 on the right. A further 3-way MANOVA test with repeated measures of topic-relevance, hemisphere, and electrode positions showed significant interaction effects, implying that the left hemisphere at central, frontal, and pre-frontal positions were sensitive in detecting topic-relevant shots while watching real time videos.

This research is for the visualization of wooden cracking phenomena in the virtual world. Timber supplies has been traditionally used for buildings in the all over the world. Wood has a characteristic that is more prone to be influenced and distorted under the external environments compared with other materials like as stone and concrete. Many related researches say that moisture content change is a largest impact factor for the distortion and crack of timber supplies. This is the reason why we focus on the relationship between the moisture variations and the wood distortion, especially crack. In this research, it is mathematically explained that the diminutions of tensile strength caused by repeated contraction and expansion according to the moisture variations under the changes of weather. Finally, we suggest a way to visualize it for pine timbers.

This paper presents a method of single image dehazing and feature matching for aerial remote sensing images. In the case of a aerial image, transferring the information of the original image is difficult as the contrast leans by the haze. This also causes that the image contrast decreases. Therefore, a refined transmission map based on a hidden Markov random field. Moreover, the proposed algorithm enhances the accuracy of image matching surface-based features in an aerial remote sensing image. The performance of the proposed algorithm is confirmed using a variety of aerial images captured by a Worldview-2 satellite.

The reliable detection of the limited defect in TFT-LCD images is difficult due to the small intensity difference with the background. However, the proposed detection method reliably detects the limited defect by enhancing the TFT-LCD image based on the cumulative histogram and then detecting the defect through the mean and standard deviation of the enhanced image. Notably, an image enhancement using a cumulative histogram increases the intensity contrast between the background and the limited defect, which then allows defects to be detected by using the mean and standard deviation of the enhanced image. Furthermore, through the comparison with the histogram equalization, we confirm that the proposed algorithm suppresses the emphasis of the noise. Experimental comparative results using real TFT-LCD images and pseudo images show that the proposed method detects the limited defect more reliably than conventional methods.

In this paper, an efficient connected component labeling (CCL) method was proposed. The proposed method is based on GPU parallelism. The CCL is very important in various applications where images are analysed. However, the label of each pixel is dependent on the connectivity of adjacent pixels so that it is not very easy to be parallelized. In this paper, a GPU-based parallel CCL techniques were proposed and applied to the analysis of radar signal. Since the radar signals contains complex and large data, the efficiency of the algorithm is crucial when realtime analysis is required. The experimental results show the proposed method is efficient enough to be successfully applied to this application.

Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

In this paper, we propose a novel security system using hand gesture recognition. Proposed system does not create a password as numbers, but instead, it creates unique yet simple pattern created by user's hand movement. Because of the fact that individuals have different range of hand movement, speed, direction, and size while drawing a pattern with their hands, the system will be able to accurately recognize only the authorized user. To evaluate the performance of our system, various patterns were tested and the test showed a satisfying result.

This paper develops a 360 degree video system using multiple off-the-shelf webcams and a set of embedded boards. Existing 360 degree cameras have shortcomings that they do not support real-time video generation since recorded videos should be copied to computers or smartphones which then provide stitching. Another shortcoming is that wide FoV(Field of View) cameras are not able to provide sufficiently high resolution. Moreover, resulting images are visually distorted bending straight lines. By employing an array of 65 degree FoV webcams, we were able to generate videos on the spot and achieve over 6K resolution with much less distortion. We describe the configuration and algorithms of the proposed system. The performance evaluation results of our early stage prototype system are presented.

As the visual effect frequently used in movies or animations, special effects are well suited for the creation of buildings or materials' destruction and collapse scenes. With the relevant programs developing technologically, the adoption of a real-time physically based-system makes it possible to realistically express dynamic simulations. In the large scale, the visual expression of such effects of destroying is satisfying enough, but most common programs of those effects fail to maximize visual effect generated with the cutting of small materials. Besides, to perform a heavy simulation process needs high-performance hardware and programs, where high costs would become a serious issue. For this reason, this paper suggests a solution optimized for the effect of small materials-cutting. The progress of each step shows technologies which trace movement with the state of the completion of the character's motions and then cut the material in real-time, finally led to the very realistic visual effect. Besides, using vector inner calculation to follow the motions of object and to realize cutting effect, this study provides an experiment that constructs visual effect for visualization from the basis of mathematical algorithm and it would be certainly as an educational material used for further researches.

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

To reduce huge losses in pig farms, weaning pigs with weak immune systems are required to be carefully supervised. Even if various researches have been performed for pig monitoring environment, segmenting each pig from touching-pigs is still entrenched as a difficult problem. In this paper, we propose a segmentation method for touching-pigs by using concave-points and edge information in a video surveillance system. Especially, we interpret the segmentation problem as a time-series analysis problem in order to identify the concave-points generated by touching-pigs. Based on the experimental results with the videos obtained from a domestic pig farm, we believe that the proposed method can accurately segment the touching-pigs.

In this paper, an iterative method is proposed to perform attenuation correction and image reconstruction simultaneously for positron emission tomography, by using the time-of-flight information. Numerical simulation results are presented to demonstrate an improved performance of the proposed method in attenuation correction and image reconstruction.

Conventional X-band marine radar has been used as one of the effective tools for collecting and retrieving ocean surface information parameters for three decades. Several wave information extracting algorithms have been designed in such a way that they can be utilized for efficiently estimating sea surface wave parameters such as current velocities, wave direction, significant wave heights in VTS (Vessel Traffic Service). However, their performances are still restricted. For the purpose of overcoming the performance limits, in this paper, first the conventional algorithms are analyzed and their performances are compared, and then a new control algorithm is proposed. Furthermore, we try to improve the estimation performances of typical wave parameters including wave directions and significant wave heights by introducing linear regression model in the process of computing wave information extraction. Through several simulations with the X-band radar images, it is shown that the proposed method is very effective in estimating the wave information compared to the real measured buoy data.

Pyramid fusion usually adjusts the Laplacian weights of pixels of the input images by evaluating predefined criteria. This has advantages that it can selectively express intense color and enhance the contrast when applied to HDR exposure fusion. But it may cause noise because the weights are determined by pixel importance without considering the interdependent pixel relationship that constitutes a scene. This paper proposes a fusion method using simple weight criteria generated from the gray pixel values, which is expected to preserve the interdependent relationship and improve execution speed. In order to evaluate the performance of the proposed method we examine a homogeneity measure, H and compare the execution time for both methods. The proposed method is found to be more advantageous with respect to homogeneity and execution speed.

In recent years, researches of security threats reported that various types of social engineering attack were frequently observed. In this paper, we propose secure keypad scheme for mobile devices. In our scheme, every edge of keypad is linked each other, and it looks like a sphere. With this keypad, users input their password using pre-selected grid pointer. Because of circulation of the keypad layout, even though the attacker snatch the user password typing motion through the human eyes or motion capture devices, attacker do not estimate the original password. Moreover, without the information of grid pointer position, the attacker do not acquire original password. Therefore, our scheme is resistant to password guessing attack.

Cross-layer design is a concept, which captures the dependencies and interactions and enables information sharing among layers in order to improve the network performance and security. There are two key challenges in wireless networks, lossy features of links and power assumption of network nodes. Cross-layer design of congestion control and power allocation in wireless lossy networks has been studied in the existing literature; however, there has been no contribution proposed in the literature that exploits the path diversity. In this paper, we are motivated to develop a cross-layer design of congestion control and power allocation, which takes into account lossy features of wireless links and transmission powers of network nodes and can be implemented in a distributed manner. Numerical simulation is conducted to illustrate the performance of our proposed algorithm and the comparison with current alternative approaches.

Interference in device-to-device (D2D) communication underlaying cellular network needs to be elaborately investigated because of channel sharing. The objective is to improve the quality of D2D communications while maintaining high performance for cellular users. In this paper, we solve the above problem by jointly considering channel allocation and power control using coalition formation game. Our cooperative game theoric approach allows to enhance network-wide performance. We design a merge-and-split algorithm to deal with the complexity of the combinatorial structure in coalition formation problem. The analytical and numerical results show that our algorithm converges to a stable point which achieves high network performance.

A person often needs to visit a facility that have not been visited before. In this case, it is necessary to easily identify the building in which the facility is located. Indoor navigation is also important, as the person may get lost due to the complex structure of buildings. To solve these problems, this paper presents a mobile application which provides a user with indoor place information and indoor navigation. A user is requested to take a photograph of a building of interest or of the current location. Then, the proposed application identifies the building of interest or the current location of the user by utilizing an image matching technique. For fast matching, database filtering by using GPS/magnetometer data and pre-extraction of features for images in the database are utilized. In case of indoor navigation, the application provides the actual images of check points as well as the moving direction. Simulation results show that the proposed system successfully identifies the location of users and provides place information and indoor navigation.

In the cases of the major cities, high poverty rate of the elderly, immature pension policies, and insufficient market conditions, policies and services for the employment of the elderly decrease the desire for the job participation. It is time to prevent the problems of the elderly, and induce the reachable seniors to participate in social activities. This research provides the location-based, customized job-search service for the elderly in order to actively support the participation in the economic activities of the elderly. The goal of SSJS(Smart Senior Job Search) is to provide the individual elderly with the customized position. It prints the appropriate positions near user location based on the residential area, job classification, and the physical condition, and provides the mash-up of the selectable job range in the unit distance based on the map. This customized service, which enables the seniors to select the type of the jobs based on their physical, mental and life conditions of the seniors, supports the participation in economic activities of the elderly people, and contribute to the expansion of the social job positions for the elderly and the equalization of the local development.

Recently Mobile Software Applications are developed in various languages and stored in App Store. App Users selectively use appropriate apps for the owned hardware. In other words, it depends on the kinds of operating systems of the apps whether to use or not to use the applications in various languages. The apps should be differently implemented according to the kind of the user's device, though they provide the same functions. To solve these problems, it is necessary to define an independent function specification method which is not dependent to a specific system environment. In this paper, the Mobile Application Developing Framework is suggested, which incorporates all of the development process. Standardized models are proposed which can be used in the analysis and design steps. In implementation phase, a technique for cross framework design is suggested so as to implement a platform dependent mobile app.

Recently, vector map has developed, used in many domains, and in most cases vector map data contains confidential information which must be kept away from unauthorized users. Moreover, the manufacturing process of a vector map is complex and the maintenance of a digital map requires substantial monetary and human resources. This paper presents the selective encryption scheme based on polyline simplification methods for GIS vector map data protection to store, transmit or distribute to authorized users. Main advantages of our algorithm are random vertices and transformation processes but it still meets requirements of security by random processes, and this algorithm can be implement to many types of vector map formats.

In this paper, we propose an energy efficient and multimedia traffic friendly MAC protocol (EEMF-MAC) that controls sender's wakeup period based on the data packet's transmission urgency and the receiver's wakeup periods based on the received data packet traffic loads. The protocol is useful for applications such as object tracking, real time data gathering, in which priority-based packet transmission is required. The basic idea of EEMF-MAC is that it uses the priority concept with transmission urgency of sender's data packet to reduce the transmission delay of the urgent data and it also uses duty cycling technique in order to achieve energy efficiency. EEMF-MAC showed a better performance in energy efficiency and packet transmission delay compared to existing protocols, RI-MAC and EE-RI-MAC.

As individuals create many contents anytime and anywhere together with the widespread dissemination of smart devices as well as various social networking services (SNS), content centric networking (CCN) has regarded as a new networking technology. However, CCN is exposed to malicious attacks on the mobility management of mobile content sources during handover and high volume of control messages. Therefore, this paper presents a secure duplicate name detection (SecureDND) mechanism without additional control messages by signed information and secure token. It is shown from the performance evaluation that the proposed scheme can provide low control overhead, which results in the network scalability.

In this study, the system to manage the power automatically was implemented by using Arduino, Raspberry pi, and Beacon technologies. Before the research, pre-research was carried out with the analysis on the existing power management systems in the market in order to find a solution to reduce burdens from standby power and power waste with the increase of electric charges. The system is designed to be able to deliver and receive data through IEEE 802.15.4 wireless protocol, by using Xbee module. Arduino was tested to verify whether it is able to control SSR(Solid State Relay), and it was found that there is no problem. Meanwhile, it was also tested whether it is possible to organize a star topology network through Arduino and Raspberry Pi, and it was confirmed that normal wireless communication is possible through IEEE 802.15.4 wireless protocol. It is designed that the signal from Android smartphone application is to be delivered to Raspberry Pi and then, to be delivered to Arduino through Xbee so that Arduino could control SSR. In addition to this, wireless protocol required to control Arduino with Raspberry Pi is also designed and applied to this research.

According to the advancement of display devices, the multi-screen contents display environment is growing to be accepted for the display exhibition area. The objectives of this research are to find communications technology and to design an editor interface of contents delivery system for the larger and adaptive multi-display workspaces. The proposed system can find existence of display devices and get information without any additional tools like marker, and can recognize device layout with only web-cam and image processing technology. The multi-display contents delivery system is composed of devices with three roles; display device, editor device, and fixed server. The editor device which has the role of main control uses UPnP technology to find existence and receive information of display devices. extract appointed color in captured picture using a tracking library to recognize the physical layout of display devices. After the device information and physical layout of display devices are connected, the content delivery system allows the display contents to be sent to the corresponding display devices through WebSocket technology. Also the experimental results show the possibility of our device connection and layout recognition techniques can be utilized for the large spaced and adaptive multi-display applications.

Explosively growing service of digital multimedia data increases the need for highly scalable low-cost storage. This paper proposes the new storage architecture based on torus network which does not need network switch and erasure coding for efficient storage usage for high scalability and efficient disk utilization. The proposed model has to compensate for the disadvantage of long network latency and network processing overhead of torus network. The proposed storage model was compared to two most popular distributed file system, GlusterFS and Ceph distributed file systems through a prototype implementation. The performance of prototype system shows outstanding results than erasure coding policy of two file systems and mostly even better results than replication policy of them.

The progress of mobile computing technology is bringing a large amount of multimedia contents such as image. Thus, we need an image retrieval system which searches semantically relevant image. In this paper, we propose a semantic image annotation and retrieval in mobile environments. Previous mobile-based annotation approaches cannot fully express the semantics of image due to the limitation of current form (i.e., keyword tagging). Our approach allows mobile devices to annotate the image automatically using the context-aware information such as temporal and spatial data. In addition, since we annotate the image using RDF(Resource Description Framework) model, we are able to query SPARQL for semantic image retrieval. Our system implemented in android environment shows that it can more fully represent the semantics of image and retrieve the images semantically comparing with other image annotation systems.

The age of autonomous vehicles has come according to development of high performance sensing and artificial intelligence technologies. And importance of the vehicle's surrounding environment sensing and observation is increasing accordingly because of its stability and control efficiency. In this paper we propose an integrated platform for efficient networking, analysis and monitoring of multiple sensing data on the vehicle that are equiped with various automotive sensors such as GPS, weather radar, automotive radar, temperature and humidity sensors. From simulation results, we could see that the proposed platform could perform realtime analysis and monitoring of various sensing data that were observed from the vehicle sensors. And we expect that our system can support drivers or autonomous vehicles to recognize optimally various sudden or danger driving environments on the road.

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

Strabismus is a non-aligned state;the visual axis of each eye is not directed toward the same direction at the same time. Clinically, the degrees of strabismus are measured by prism cover test, corneal reflex test (Hirschberg test), prism reflex test (Krimsky prism test), But corneal reflex test and prism reflex test is a possibility that errors occur. we suggest a computer-aided diagnosis for strabismus. We made a mobile application to measure angles of strabismus. For 34 patients, we tested our application. The result of comparing between two methods, It showed a difference 7 Prism Diopter(PD). Our application gives strabismus angles just using a camera and a smart device. Therefore, it can reduce the cost and make the diagnosis of strabismus accurate.

If the undo/redo function is not reflected in the initial design of an application, it makes it difficult to implement the undo/redo function additionally, in this paper, we examined some examples to design the sodoku game and analyzed problems of the design to implement the undo/redo functions. For an efficient design of the undo/redo functions without using swing.undo package, we propose a class design using the Command, Memento, and Observer pattern these are used as organic. The proposed method is more efficient for distributed work than other method. We implemented a sudoku game using proposed design. In the undo/redo function testing, we could see that it works well.

Thesedays, not only a quality of a product but also a design of it plays an important role. Throughout this analysis, I would like to discuss how a design of package works in the cosmetics with the fact that low cost cosmetics sales better than high cost cosmetics. In this research, there are five samples each for Korean and Chinese low-costed cosmetics to yield a result about an element of package design. Based on that result, there was an survey about two different package design followed by the research problems. Throughout research, an attention and familiarity of package which was considered of characteristics of China was more effective on Chinese consumers. In order to activate the content market of cosmetics, it is utilized to manufacture taking into account the maximum of the design elements in the package design is a conclusion that affect the time of purchase. In this paper, one in which I hope to help in the development of being utilized as a basis article content market industry for pre-market research foray into the Chinese market is South Korean cosmetics.

With the spread of social media and mobile devices, people spend more time on online than ever before. As more people participate in various online activities, much research has been conducted on how to make use of the time effectively and productively. In this paper, we propose two methods which can be used to extract highlights and make searchable media indexes using online social data. For highlight extraction, we collected the comments from the online baseball broadcasting website. We adopted peak-finding algorithm to analyze the frequency of comments uploaded on the comments section of the website. For each indexes, we collected postings from soap opera forums provided by a popular web service called DCInside. We extracted all the instances when a character's name is mentioned in postings users upload after watching TV, which can be used to create indexes when the character appears on screen for the given episode of the soap opera The evaluation results shows the possibility of the crowdsourcing-based media interaction for both highlight extraction and index building.

The aims of this research is to build Customer Delight index contents model for measuring the influential factors - Customer Delight and identify the relationship among Customer Delight, Customer Satisfaction and Customer Loyalty. Customer Delight hypothesis model was proposed by Catering Service Industry of Starbucks and validate the final questionnaire; 7 point Likert scale was used in the questionnaire, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) are used to analyze the reliability. And path analysis was used to evaluating the final hypothesis model. The results of this research was that the customer Self-involvement can lead to high awakening level and it also bring positive emotion to the customer. Awakening Level and positive emotion were the key factors for the Customer Delight. Instead of customer expectation, Customer Delight is based on customer awakening level and positive emotion which is different from Customer Satisfaction model. It is clearly to see the discrepancies between Customer Delight and Customer Satisfaction.

This paper presents an interactive locomotion controller using motion capture data and inverted pendulum model. Most of the data-driven character controller using motion capture data have two kinds of limitation. First, it needs many example motion capture data to generate realistic motion. Second, it is difficult to make natural-looking motion when characters navigate dynamic terrain. In this paper, we present a technique that uses dimension reduction technique to motion capture data together with the Gaussian process dynamical model (GPDM), and interpolates the low-dimensional data to make final motion. With the low-dimensional data, we can make realistic walking motion with few example motion capture data. In addition, we apply the inverted pendulum model (IPM) to calculate the root trajectory considering the real-time user input upon the dynamic terrain. Our method can be used in game, virtual training, and many real-time applications.

Recently, a number of solutions were proposed to address the challenges and issues of vehicular networks. Vehicular Cloud Computing (VCC) is one of the solutions. The vehicular cloud computing is a new hybrid technology that has a remarkable impact on traffic management and road safety by instantly using vehicular resources. In this paper, we study an important vehicular cloud service, content-based delivery, that allows future vehicular cloud applications to store, share and search data totally within the cloud. We design a VCC-based system architecture for efficient sharing of vehicular contents, and propose a Hierarchical Hybrid Content Delivery scheme using Bloom Filter (H2CDBF) for efficient vehicular content delivery in Vehicular Ad-hoc Networks (VANETs). The performance of the proposed H2CDBF is evaluated through an analytical model, and is compared to the proactive content discovery scheme, Bloom-Filter Routing (BFR).

is a service providing the story synopses that match user's query. This paper analyzes the user log of which is the answers to the queries to find stories from database, and shows the tendency distribution of user creation. Specially, we analyze a joint distribution of the genres and actions of stories to get better understanding of the tendencies that cannot be found in the analysis of independent distribution. Furthermore, we define a correlation factor between genre and action, and investigate what combinations of the genres and actions are highly, less, and negatively correlated. Finally, we investigate how the tendencies of characters are related to genres and actions, and propose a visualization method to show the tendencies.

Recently, as the public interests about the 3D printing technology are increased, various kinds of 3D printers are being released. But, they are limited to use because they cannot fabricate an object which is larger than the printer's printing volume. To relieve this problem, we propose an interactive 3D mesh editing system for 3D printing the object that is larger than the printing volume. The proposed 3D editing system divides the input 3D mesh using the user's line drawings defining cutting planes and it attaches various connectors. The output meshes are guaranteed to fabricate without post-processing. The printed parts can be assembled using the connectors. Our proposed system has an advantage that it can be used easily by non-professional 3D printer users.

This study examines whether online game motivation, with social and expedition needs, are associated with the degree of loneliness among young adolescents. Using data from a survey of 2014 elementary-, middle- and high-school students in South Korea, a three-wave online survey was conducted to collect data from online players. Two latent growth cause-and-effect models were developed to test whether online game motivation influences degree of loneliness. Results showed that both social and expedition needs are positively associated with loneliness. This study contributes to the knowledge of the underlying needs for a player with loneliness and also highlights the social and expedition needs as potential therapeutic factors.

In navigation systems that use collected trajectory for routing, the number and diversity of trajectory data are crucial despite the infeasible limitation which is that all routes should be collected in person. This paper suggests an algorithm combining trajectories only by collected GPS data and generating new routes for solving this problem. Using distance between two trajectories, the algorithm estimates road intersection, in which it also predicts the correlated direction of them with geographical coordinates and makes a decision to combine them by the correlated direction. With combined and generated trajectory data, this combination way allows trajectory-based navigation to guide more and better routes. In our study, this solution has been introduced. However, the ways in which correlated direction is decided and post-process works have been revised to use the sequential pattern of triangles' area GPS information between two trajectories makes in road intersection and intersection among sets comprised of GPS points. This, as a result, reduces unnecessary combinations resulting redundant outputs and enhances the accuracy of estimating correlated direction than before.