KJC Korea
Journal Central

More new devices and technologies are recently adapted on homes to aim at enhancing our lifestyle. But though they are not interconnected each other but controlled separately. To solve this problem, we have designed and implemented a common prototype based on openHAB and RaspberryPi they could speak to each other to create a really automated and smart environment at home. The proposed prototype can merge the existing variable devices, add and remove new features at runtime because of its modular design. The proposed prototype based on a low-cost platform showed its potential as a smart home and provide a new UX to users.

This paper presents an efficient FTL for NAND flash based SSDs. Address translation information of page mapping based FTLs is stored on flash memory pages and address translation cache keeps frequently accessed entries. The proposed FTL of this paper reduces response time by considering both of temporal and spacial localities of page access patterns in translation cache management. The localities of several well-known traces are evaluated and determine the structure of the cache for high hit ratio. A simulation with several well-known traces shows that the presented FTL reduces response time in comparison to previous FTLs and can be used with relatively small size of caches.

In this paper, we have implemented a low-cost mobile robot supporting the person tracking by detection using RGB-D cameras and ROS(Robot Operating System) framework. The mobile robot was developed based on the Kobuki mobile base equipped with 2’s Kinect devices and a high performance controller. One kinect device was used to detect and track the single person among people in the constrained working area by combining point cloud data filtering & clustering, HOG classifier and Kalman Filter-based estimation successively, and the other to perform the SLAM-based navigation supported in ROS framework. In performance evaluation, the person tracking by detection was proved to be robustly executed in real-time, and the navigation function showed the accuracy with the mean distance error being lower than 50mm. The mobile robot implemented has a significance in using the open-source based, general-purpose and low-cost approach.

Design of an intelligent controller for efficient control in smart building is one of the effective technologies to reduce energy consumption by reducing response time with keeping comfortable level for inhabitants. In this paper, we focus on how to find major parameters in order to enhance the ability of HVAC(heating, ventilation, air conditioning) PID controller. For the purpose of that, we use machine learning technologies for tuning HVAC devices. We show the simulation results to illustrate the behavioral relation of whole system and each control parameter while learning process.

Extracting temporal information from documents is becoming more important, because it can be used to various applications such as Question-Answering (QA) systems, Recommendation systems, or Information Retrieval (IR) systems. Most previous studies only focus on English documents, and they are not applicable to the other languages due to the inherent characteristics of languages. In this paper, we propose a new system, named ExoTime, designed to extract temporal information from Korean documents. The ExoTime adopts an external Knowledge Base (KB) in order to achieve better prediction performance, and it also applies a bagging method to the temporal relation prediction. We show that the effectiveness of the proposed approaches by empirical results using Korean TimeBank. The ExoTime system works as a part of ExoBrain that is an artificial intelligent QA system.

Pathfinding for pedestrians provided by various navigation programs is based on a shortest path search algorithm. There is no big difference in their guide results, which makes the path quality more important. Multiple criteria should be included in the search cost to calculate the path quality, which is called a multi-criteria pathfinding. In this paper we propose a user adaptive pathfinding algorithm in which the cost function for a multi-criteria pathfinding is defined as a weighted sum of multiple criteria and the weights are learned automatically by Perceptron learning. Weight learning is implemented in two ways: short-term weight learning that reflects weight changes in real time as the user moves and long-term weight learning that updates the weights by the average value of the entire path after completing the movement. We use the weight update method with momentum for long-term weight learning, so that learning speed is improved and the learned weight can be stabilized. The proposed method is implemented as an app and is applied to various movement situations. The results show that customized pathfinding based on user preference can be obtained.

In this paper, we propose a novel tracking method using target separation and detection that are based on discriminative correlation filter (DCF), which is studied a lot recently. ‘Retainability’ is one of the most important factor of tracking. There are some factors making retainability of tracking worse. Especially, fast movement and occlusion of a target frequently occur in image data, and when it happens, it would make target lost. As a result, the tracking cannot be retained. For maintaining a robust tracking, in this paper, separation of a target is used so that normal tracking is maintained even though some part of a target is occluded. The detection algorithm is executed and find new location of the target when the target gets out of tracking range due to occlusion of whole part of a target or fast movement speed of a target. A variety of experiments with various image data sets are conducted. The algorithm proposed in this paper showed better performance than other conventional algorithms when fast movement and occlusion of a target occur.

In this paper, we propose a bounding box prediction algorithm using multiple probability maps to improve object detection result of object detector. Although the performance of object detectors has been significantly improved, it is still not perfect due to technical problems and lack of learning data. Therefore, we use the result correction method to obtain more accurate object detection results. In the proposed algorithm, the preprocessed bounding box created as a result of object detection by the object detector is clustered in various form, and a conditional probability is given to each cluster to make multiple probability map. Finally, multiple probability map create new bounding box of object using morphological elements. Experiment results show that the newly predicted bounding box reduces the error in ground truth more than 45% on average compared to the previous bounding box.

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

The purpose of this study is to design and develop of digital contents to improve computational thinking in the online education environment. First, we planned the design and development of contents with 19 experts of Software education. Digital content was designed from the point of view of improving the educational quality and the quality of contents for the improve of computing thinking. The content type is classified into the SW education area; computer science, programming, physical computing, convergent computing, computing thinking, and software education that improves the computing thinking. And we designed 45 learning programs for each SW education area. Designed learning contents were developed in 464 lessons to suit the online education environment. The content validity of the proposed content was verified by the expert group and the average CVI value was over .83. Through this, we could analyze that the developed contents will help learners to expand their computing thinking.

In this paper, we propose a zone based ad hoc network construction scheme which support ad hoc path between nodes in local IoT networks consisting of IoT devices with the constrained feature, such as low power, the limited transmission rate and low computing capacity. Recently, the various routing protocols have been studied to support ad hoc networking of local IoT environments. This is, because basis RPL protocol is deigned to be used for the connecting service with Internet through gateway, ad hoc path between nodes in local IoT networks is not supported in basis RPL protocol. Thus, in this paper, the proposed routing scheme provides both ad hoc path and Infra path through gateway, supporting basis RPL protocol simultaneously. Through simulation, we show that the proposed routing scheme with zone based path selection scheme improves the performance of the success rate of end-to-end data transmission and the end-to-end delay, compared to basis RPL protocol.

The study proposed a system that filters the data that is entered when analyzing big data such as SNS and BLOG. Personal information includes impersonal personal information, but there is also personal information that distinguishes it from personal information, such as religious institution, personal feelings, thoughts, or beliefs. Define these personally identifiable information as sensitive information. In order to prevent this, Article 23 of the Privacy Act has clauses on the collection and utilization of the information. The proposed system structure is divided into two stages, including Big Data Processing Processes and Sensitive Information Filtering Processes, and Big Data processing is analyzed and applied in Big Data collection in four stages. Big Data Processing Processes include data collection and storage, vocabulary analysis and parsing and semantics. Sensitive Information Filtering Processes includes sensitive information questionnaires, establishing sensitive information DB, qualifying information, filtering sensitive information, and reliability analysis. As a result, the number of Big Data performed in the experiment was carried out at 84.13%, until 7553 of 8978 was produced to create the Ontology Generation. There is considerable significan ce to the point that Performing a sensitive information cut phase was carried out by 98%.

Short-lived pseudonym certificates as vehicle identities could satisfy both security and privacy requirements. However, to remove revoked certificates especially in vehicle communications, pseudonym certificate revocation list (CRL) should be distributed resource-efficiently from a practical deployment point of view and in a timely manner. In this paper, we propose a novel CRL distribution scheme capable of CRL multicast to only activated vehicles registered to the CRL multicast group using the group communication system enabler, namely, the GCSE which is being standardized. The scheme is resource efficient by using CRL distribution paths instead of paging processes to find out multicast vehicle(s) within a certain region. The analyzed results show that the proposed scheme outperforms in terms of paging cost, packets transmission cost, and the processing cost at the respective entities compared to the existing four schemes in the literature.

The personalized search algorithm is a search system that analyzes the user's IP, cookies, log data, and search history to recommend the desired information. As a result, users are isolated in the information frame recommended by the algorithm. This is called 'Filter bubble' phenomenon. Most of the personalized data can be deleted or changed by the user, but data stored in the service provider‘s server is difficult to access. This study suggests a way to neutralize personalization by keeping on sending random query words. This is to confuse the data accumulated in the server while performing search activities with words that are not related to the user. We have analyzed the rank change of the URL while conducting the search activity with 500 random query words once using the personalized account as the experimental group. To prove the effect, we set up a new account and set it as a control. We then searched the same set of queries with these two accounts, stored the URL data, and scored the rank variation. The URLs ranked on the upper page are weighted more than the lower-ranked URLs. At the beginning of the experiment, the difference between the scores of the two accounts was insignificant. As experiments continue, the number of random query words accumulated in the server increases and results show meaningful difference.

Screw insertion surgery is frequently required among surgical procedures. Especially, very careful attention should be paid to the insertion of screw in the operation of the cervical vertebra. Therefore, there is a need for a guide that allows the surgeon to reliably and promptly perform treatment by calculating the desired insertion angle and length for screw insertion. In this study, the center and direction of the pedicle were calculated through 3D modeling and 3D vector numerical analysis using the CT or MRI image of the patient for the safe operation of the guide, and based on this, After that, we will implement surgical guide based on this.

PLM(Product Lifecycle Management) is an information management system that can integrate data, processes, business systems and human resources throughout the enterprise. BOM(Bills Of Material) is key data for designing, purchasing materials, manufacturing planning and management, which is basic for product development throughout the product life cycle. In this paper, we propose the efficient system to increase the data loading speed and the processing speed when using such large BOM data. We present the performance and usability of IMDG (In Memory Data Grid) for data processing when loading large amounts of data. In the UI, using the pure web grid of JavaScript instead of the existing data loading method can be improve performance of data managing.

In this paper, we propose optimal route searching algorithm using ANN(Artificial Neural Network) and implement route searching system. Our proposed scheme shows that the route using artificial neural network is almost same as the route using Dijkstra’s algorithm but the time in our propose algorithm is shorter than that of existing Dijkstra’s algorithm. Proposed route searching method using artificial neural network has better performance than exiting route searching method because it use several weight value in making different routes. Through simulation, we show that our proposed routing system improves the performance and reduces time to make route irrespective of the number of hidden layers.

Systemic management is required at each phase of the C4I system life cycle to achieve information advantage in the battlefield through stable operation of the C4I system under the NCOE. In particular, the maintenance phase is the longest period of the C4I system life cycle, and it is easy to utilize and analyze data such as faults that occur during system operation. However, according to the previous research, the maintenance is evaluated as a phase in which the definition and management of comprehensive indicators are insufficient compared to other phase. In this paper, we propose the method of C4I Maintenance Priority decision using Maintenance metric. As a result of modifying the Naval Tactical C4I System's preventive maintenance cycle according to the priority, the total number of faults is reduced and the maintenance efficiency is improved.

Recently, as the number of multicultural families and foreigners living in korea increase, the proportion of various types of families and middle-admitted youths is increasing. These youths are less educated than their domestic counterparts, and their conversation time with their family members is relatively weak. Therefore, there is a need for a specialized education system for education and socialization. Immigration background among middle-admitted adolescents, children arrived in korea regardless of their will, with socialization already in the country where they were born, it is a reality that various difficulties are experienced in the socialization of korea society about language, education, emotion and employment. For this reason, some of the migrant background youths are pointed out as a big problem of the multicultural society, which is 18% of the NEET(Not in Education, Employment or Training) classes, which are not educated and are not willing to find jobs or employment. Therefore, in this study, we identified the problems of middle-admitted children of multicultural families as the number of middle-admitted adolescents increased, and suggested the necessary ways for them to achieve rapid socialization and settlement in korea society. For this purpose, we analyzed the problem of education of middle-admitted children as a discriminative approach which is different from general support method for middle-admitted children presented in previous reaearch, since then, we have presented an alternative to carry out realistic, systematic and successful education considering the characteristics of the region centered on the middle-admitted youths of Gwangju city.

The purpose of this study is to introduce policy and theoretical implications by analyzing affecting factors for the elder’s happiness. For this study, we analyzed data using HLM. Data include a world value survey(hereafter, WVS) as personal level analysis data and also OECD's Social Expenditure Database(hereafter, SOCX) and database from the World Bank as national level analysis data. The subjects of personal level analysis were the elder who are over 65-years od age, and they were total 3,297 people, and while the subjects of national level analysis were total 9 OECD countries. For the data analysis, hierarchial linear model(HLM) analysis was done by using HML 7.0 program. As a result of analysis, First, for the elderly 's happiness, they should improve self-disposition, members of social groups, and social class. Second, the old-age pension and the survivor's pension had no meaningful effect on the happiness. but it was found that self - disposition, social class, gender, and health status showed meaningful interaction effect according to old - age pension, survivor pension, per capita GDP, income inequality. This suggests that efforts to improve the happiness of the elderly should be made at the individual level and the national level at the same time.

Investigations into family-friendly company culture have been carried out for various companies and conglomerates. But there has never been such investigation for police organization, so this investigation is intended to see the effect on organizational commitment and job performance of the organization culture that makes use of family-friendly program for the police organization. To this end, how the internal factors of family-friendly organization culture affects organization commitment and job performance is empirically analyzed with structural equation modeling. And it is found that family-friendly program has significant effect on organizational life and welfare, and the welfare, in return, is found to have significant effect on job performance.

Managing students' data is a high prioritized duty of the university administration since most of the school affairs are proceed based on that database. Universities have invested in IT assets such as servers, storage, database, and networks. However, continuing investment in IT infrastructure is impossible due to limited budget and rapid changes in the educational environment. As cloud computing diffuse, universities are trying to reduce costs and improve efficiency by increasing server utilization, unlike when physically investing. We designed a hypothetical academic information management system based on cloud computing by utilizing the advanced server virtualization technology. This administrative cloud system allows universities to improve the availability of the system with low cost. The system demonstrates its flexibility of using data resources and immediacy of resumption.