Earticle

최근 IoT와 연계된 서비스들이 다양한 환경에서 활용되면서 IoT와 인공지능 기술이 융합되고 있다. 그러나, IoT 데이터를 안정적으로 처리하는 기술들이 완벽하게 지원되고 있지 않아 이를 위한 연구가 필요한 상황이다. 본 논문에서는 IoT 데이터를 머신러닝 기반으로 임베디드 벡터를 생성한 후 IoT 데이터를 최적화 할 수 있는 처리 기법을 제안한다. 제안 기법에서는 처리 효율을 위해서 IoT 데이터의 인덱스, 수집 위치(X와 Y축 좌표의 이진값), 그룹 인덱스, 타입, 종류 등을 QR 기반으로 임베디드 벡터화를 수행한다. 또한, IoT 데이터를 비대칭적으로 연계하 도록 IoT 데이터 수집 과정에서 로드밸런싱을 수행할 수 있도록 다양한 IoT 장치에서 생성한 데이터를 통합 관리한 다. 제안 기법은 비대칭적으로 IoT 데이터를 그룹화할 수 있도록 IoT 데이터를 해쉬기반으로 서로 직교화하도록 처리한다. 또한, IoT 데이터 종류 및 특성에 따라 주기적으로 생성 및 그룹화하기 때문에 IoT 데이터 간 간섭은 최소화할 수 있다. 향후 연구에서는 IoT 서비스를 제공하는 여러 환경에서 제안 기법을 비교 평가할 계획이다.

Recently, IoT-linked services have been used in various environments, and IoT and artificial intelligence technologies are being fused. However, since technologies that process IoT data stably are not fully supported, research is needed for this. In this paper, we propose a processing technique that can optimize IoT data after generating embedded vectors based on machine learning for IoT data. In the proposed technique, for processing efficiency, embedded vectorization is performed based on QR such as index of IoT data, collection location (binary values of X and Y axis coordinates), group index, type, and type. In addition, data generated by various IoT devices are integrated and managed so that load balancing can be performed in the IoT data collection process to asymmetrically link IoT data. The proposed technique processes IoT data to be orthogonalized based on hash so that IoT data can be asymmetrically grouped. In addition, interference between IoT data may be minimized because it is periodically generated and grouped according to IoT data types and characteristics. Future research plans to compare and evaluate proposed techniques in various environments that provide IoT services.

최근에는 디지털 헬스케어 기기가 사용자 중심으로 발전하고 있으며, 이러한 발전은 재활치료 분야에도 적용되 고 있다. 재활치료는 반복적이고 장기적인 과정을 요구하며, 일반적으로 의료진의 감독하에 병원에서 이루어지기 때문 에 사용자 중심의 상시적인 치료가 어려운 경우가 많다. 그러나 최근 ICT 기술 융합을 기반으로 한 바이오센서를 활용 한 생체신호 측정과 분석은 환자 상태를 파악하고 재활 운동을 개선하는데 도움이 된다. 본 연구에서는 뇌졸중 환자를 대상으로 하는 실시간 근기능 평가 시스템을 소개하고, 이 시스템의 실시간 데이터 처리 성능을 분석하였다. 이 시스템 은 중앙 서버와 통신하는 오프로딩 방식이 아닌 온-디바이스 실시간 데이터 처리 기능을 사용하며, EMG 데이터를 활용하여 근기능을 평가한다. 실험 결과에서 데이터 처리 속도와 데이터 처리 시 윈도우 크기와 시프트 개수의 중요성 을 분석하였고 이를 통해 실시간 근기능 평가를 통한 개별 맞춤형 재활치료 서비스 개발이 가능함을 제시한다.

Digital healthcare devices adopt a user-centric approach in rehabilitation therapy, though its repetitive, long-term nature limits it to clinical settings. The integration of ICT technology, particularly biosensors, has enabled objective bio-signal measurement and analysis, offering insights for patient assessment and rehabilitation. In this study, we introduce a real-time muscle function assessment system designed for stroke patients and conduct an analysis of its real-time data processing capabilities. This system employs on-device real-time data processing. Muscle function assessment is achieved through the utilization of electromyography (EMG) data. Our experimental findings shed light on the pivotal role of data processing speed, window size, and shift parameters. This suggests the potential for the development of personalized rehabilitation services through real-time muscle function assessment.

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of Internet of Things (IoT) technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatialtemporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

It is important to understand the exact habitat distribution of endangered species because of their decreasing numbers. In this study, we build a system with a deep learning module that collects the image data of endangered animals, processes the data, and saves the data automatically. The system provides a more efficient way than human effort for classifying images and addresses two problems faced in previous studies. First, specious answers were suggested in those studies because the probability distributions of answer candidates were calculated even if the actual answer did not exist within the group. Second, when there were more than two entities in an image, only a single entity was focused on. We applied an object detection algorithm (YOLO) to resolve these problems. Our system has an average precision of 86.79%, a mean recall rate of 93.23%, and a processing speed of 13 frames per second.

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

As cloud computing has become a widespread technology, malicious attackers can obtain the private information of users that has leaked from the service provider in the outsourced databases. To resolve the problem, it is necessary to encrypt the database prior to outsourcing it to the service provider. However, the most existing data encryption schemes cannot process a query without decrypting the encrypted databases. Moreover, because the amount of the data is large, it takes too much time to decrypt all the data. For this, Programmable Order-Preserving Secure Index Scheme (POPIS) was proposed to hide the original data while performing query processing without decryption. However, POPIS is weak to both order matching attacks and data count attacks. To overcome the limitations, we propose a group order-preserving data encryption scheme (GOPES) that can support efficient query processing over the encrypted data. Since GOPES can preserve the order of each data group by generating the signatures of the encrypted data, it can provide a high degree of data privacy protection. Finally, it is shown that GOPES is better than the existing POPIS, with respect to both order matching attacks and data count attacks.

Related to the maximum vector problem, a skyline query is to discover dominating tuples from a set of tuples, where each defines an object (such as a hotel) in several dimensions (such as the price and the distance to the beach). A tuple, an instance of an object, dominates another tuple if it is equally good or better in all dimensions and better in at least one dimension. Traditionally, skyline queries are defined upon single-instance data or upon objects each of which is associated with an instance. However, in some cases, an object is not associated with a single instance but rather by multiple instances. For example, on a review website, many users assign scores to a product or a service, and a user's score is an instance of the object representing the product or the service. Such data is an example of multi-instance data. Unlike most (if not all) others considering the traditional setting, we consider skyline queries defined upon multi-instance data. We define the dominance calculation and propose an algorithm to reduce its computational cost. We use synthetic and real data to evaluate the proposed methods, and the results demonstrate their utility.

이 연구는 4차 산업혁명과 디지털 사회의 도래 속에서 설교가 공공 담론으로서의 기능을 회복하기 위한 방안을 모색한 것이다. 전통적 설 교가 교회 내부 중심의 언어 구조와 교리 중심 메시지에 머무르며 교회 밖 수신자와의 소통에 한계를 드러낸다는 문제의식 속에서, 본 연구는 빅데이터와 인공지능(AI)을 기반으로 한 설교 설계 전략이 공공성을 회복할 수 있는 대안이 될 수 있는지를 탐색하였다. 설문조사와 유튜브 설교 분석을 통해 수신자의 현실적 상황성(경제적 불안, 정서적 고립 등)이 설교 수용성에 밀접하게 연관되어 있음을 확인하였으며, 데이터 기반의 수요 맞춤형 설교 시리즈는 청중의 높은 공감과 반응을 유도하 는 데 효과적임을 실증적으로 제시하였다. 또한 AI 설교 도구와 인간 설교자의 협업 가능성을 평가하며, 기술 활용 능력과 신학적 통찰의 병 행이 설교자의 필수 역량임을 주장하였다. 결론적으로 본 연구는 기술 과 영성이 통합된 설교 생태계의 필요성, 그리고 설교자가 단지 본문 해석자에 머무르지 않고 복음의 문화 번역자, 공공신학적 중재자로서의 역할을 감당해야 함을 제안한다. 이를 위해 신학교 및 설교자 양성 기관의 커리큘럼 개편과, 설교자 대상 디지털 감수성 훈련의 필요성도 함께 제언하였다.

This study seeks ways to restore the function of sermons as public discourse in the era of the Fourth Industrial Revolution and the advent of digital society. With the awareness that traditional sermons remain limited to internal church-centered language structures and doctrine-centered messages, and show limitations in communication with recipients outside the church, this study explores whether sermon design strategies based on big data and artificial intelligence (AI) can be an alternative to restore publicness. Through a survey and analysis of YouTube sermons, it was confirmed that the recipients’realistic situation (economic anxiety, emotional isolation, etc.) is closely related to sermon acceptance, and it was empirically suggested that a data-based demand-tailored sermon series is effective in inducing high empathy and response from the audience. In addition, it evaluated the possibility of collaboration between AI sermon tools and human preachers, and argued that the ability to utilize technology and theological insight are essential competencies for preachers. In conclusion, this study suggests the need for a sermon ecosystem that integrates technology and spirituality, and that preachers should not remain mere interpreters of the text, but should fulfill their roles as cultural translators of the gospel and mediators of public theology. To this end, the need for curriculum reform in seminaries and preacher training institutions, as well as digital sensitivity training for preachers, was also suggested.

In this paper, we have proposed an architectural solution for a system for the visualization and modification of large amounts of data. The pattern is based on an asynchronous execution of programmable commands and a reflective approach of an object structure composition. The described pattern provides great flexibility, which helps adopting it easily to custom application needs. We have implemented a system based on the described pattern. The implemented system presents an innovative approach for a dynamic data object initialization and a flexible system for asynchronous interaction with data sources. We believe that this system can help software developers increase the quality and the production speed of their software products.

We report processing technique in the MO image measurement system. Calibration procedure is not only considered to perpendicular field but also in-plane field. Current density and field profiles are obtained by Biot-savart law and inversion method. We show example of (Gd,Y)1Ba2Cu3O7-δ-BaZrO3 film that have tilted nano rod pinning centers about 13° from the c-axis.

Early development of listening ability has been viewed as vital in the field of teaching English as a Foreign/Second Language (EFL/ESL). Both researchers and teachers have thus been greatly concerned with finding some ways to improve the learner’s listening competence, or better ideas to help learners acquire listening skills in the formal classroom setting. As one way of achieving this goal, this study spells out advantages of using technologically up-to-date audio/video data processing softwares over traditional audio/video players or CD/DVD players. These traditional language teaching aids have been of use and still can be to some extent. But they have been somewhat limited in terms of easier retrieval of necessary language input at the teacher’s will. For instance, when teachers try to use some specific parts of audio-video tapes for test construction, they have to spend a lot of time and energy cutting and pasting them. In addition, the audio/video quality often decreases in the process due to some technical problems. This study shows that both Digital Sound Processors (DSP) like Adobe Audition and Visual Data Processors (VDP) like Virtual Dub can be used so as to overcome those ever-existing limitations and thus to enhance ways of teaching listening. As a case study, this study comes up with a few practical ideas of how to apply language teaching materials made by using audio/video data processing programs to teaching listening, particularly for adult EFL learners.

Method for providing a human interface for media services in a user equipment. In order to provide the human interface, the background screen of the human interface may be formed from at least one image. Each image of the at least one image may represent a respective one of the retrieved media services. The human interface having the background screen formed from the at least one image may be displayed. A desired image may be selected from the at least one image of the displayed human interface, and a desired media service corresponding to the selected desired image may be played.

최근 많은 기관들로부터 클라우드 서비스, 빅 데이터가 향후 대세적인 IT 트렌드 및 확고한 기술로서 예견되고 있다. 또한 현재 IT를 선도하는 많은 벤더를 중심으로 클라우드, 빅데이터에 대한 실질적인 솔루션과 서비스를 제공하고 있다. 이러한 기술들은 기업의 비용절감 측면에서, 클라우드는 인터넷 기반의 다양한 기술들을 기반으로 비즈니스 모델에 대한 자원의 사용을 자유스럽게 선택할 수 있는 장점을 가지고 있어 능동적인 자원 확장을 위한 프로비져닝 기술과 가상화 기술들이 주요한 기술로 주목 받고 있다. 또한 빅데이터는 그동안 분석하지 못했던 새로운 비정형 데이터들에 대한 분석 환경을 제공함으로서 데이터 예측모델의 차원을 한층 높이고 있다. 하지만 클라우드 서비스, 빅데이터의 공통점은 대용량 데이터를 기반으로 서비스 또는 분석을 요하고 있어, 초기 발전 모델부터 대용량 데이터의 효율적인 운영 및 설계가 중요하게 대두 되고 있다. 따라서 본 논문에 클라우드, 빅데이터 서비스를 위한 대용량 데이터 기술 요건들을 토대로 데이터 처리 아키텍처를 정립하고자 한다. 특히, 클라우드 컴퓨팅을 위해 분산 파일 시스템이 갖추어야 할 사항들과 클라우드 컴퓨팅에서 활용 가능한 오픈소스 기반의 하둡 분산 파일 시스템, 메모리 데이터베이스 기술요건을 소개하고, 빅데이터, 클라우드의 대용량 데이터를 비용절감 측면에서 효율적인 압축기술 요건들을 제시한다.

In recent years, many institutions predict that cloud services and big data will be popular IT trends in the near future. A number of leading IT vendors are focusing on practical solutions and services for cloud and big data. In addition, cloud has the advantage of unrestricted in selecting resources for business model based on a variety of internet-based technologies which is the reason that provisioning and virtualization technologies for active resource expansion has been attracting attention as a leading technology above all the other technologies. Big data took data prediction model to another level by providing the base for the analysis of unstructured data that could not have been analyzed in the past. Since what cloud services and big data have in common is the services and analysis based on mass amount of data, efficient operation and designing of mass data has become a critical issue from the early stage of development. Thus, in this paper, I would like to establish data processing architecture based on technological requirements of mass data for cloud and big data services. Particularly, I would like to introduce requirements that must be met in order for distributed file system to engage in cloud computing, and efficient compression technology requirements of mass data for big data and cloud computing in terms of cost-saving, as well as technological requirements of open-source-based system such as Hadoop eco system distributed file system and memory database that are available in cloud computing.