• Journal of Information Processing Systems
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• 제16권4호
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• pp.743-749
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• 2020

Internet of Things (IoT) is a growing technology along with artificial intelligence (AI) technology. Recently, increasing cases of developing knowledge services using information collected from sensor data have been reported. Communication is required to connect the IoT and AI, and 5G mobile networks have been widely spread recently. IoT, AI services, and 5G mobile networks can be configured and used as sensor-mobile edge-server. The sensor does not send data directly to the server. Instead, the sensor sends data to the mobile edge for quick processing. Subsequently, mobile edge enables the immediate processing of data based on AI technology or by sending data to the server for processing. 5G mobile network technology is used for this data transmission. Therefore, this study examines the challenges, opportunities, and solutions used in each type of technology. To this end, this study addresses clustering, Hyperledger Fabric, data, security, machine vision, convolutional neural network, IoT technology, and resource management of 5G mobile networks.

• Wind and Structures
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• 제16권2호
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• pp.157-178
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• 2013

Output-only modal parameter identification is based on the assumption that external forces on a linear structure are white noise. However, harmonic excitations are also often present in real structural vibrations. In particular, it has been realized that the use of forced acceleration responses without knowledge of external forces can pose a problem in the modal parameter identification, because an external force is imparted to its impulse acceleration response function. This paper provides a three-stage identification procedure as a solution to the problem of harmonic and white noise excitations in the acceleration responses of a linear dynamic system. This procedure combines the uses of the mode indicator function, the complex mode indication function, the enhanced frequency response function, an iterative rational fraction polynomial method and mode shape inspection for the correlation-related functions of the force-embedded acceleration responses. The procedure is verified via numerical simulation of a five-floor shear building and a two-dimensional frame and also applied to ambient vibration data of a large-span roof structure. Results show that the modal parameters of these dynamic systems can be satisfactorily identified under the requirement of wide separation between vibration modes and harmonic excitations.

• 설비공학논문집
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• 제28권3호
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• pp.103-109
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• 2016

The purpose of this study is to perform comparative economic evaluation for the systems of ground source heat pump (GSHP) and district heating and cooling (DHC) by focusing on the analysis of operation case of GSHP. The adapted research object is a public office building located in Seoul. The capacity of ground source pump is about 3,900 kW. Ground heat exchanger is closed loop type. The analysis period for life cycle cost is 30 years. Economic evaluation is assessed from the viewpoints of the following four parts: initial cost, energy cost, maintenance and replacement cost, and environment cost. The total life cycle cost of GSHP is approximately 8,447 million won. The cost of the DHC System is approximately 3,793 million won. The cost of the DHC is approximately 46% lower than GSHP system under the condition of current rate for GSHP and DHC.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2732-2753
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• 2019

With the application of wireless sensor networks in the fields of ecological observation, defense military, architecture and urban management etc., the security problem is becoming more and more serious. Characteristics and constraint conditions of wireless sensor networks such as computing power, storage space and battery have brought huge challenges to protection research. Inspired by the danger theory in biological immune system, this paper proposes an intrusion detection model for wireless sensor networks. The model abstracts expressions of antigens and antibodies in wireless sensor networks, defines meanings and functions of danger signals and danger areas, and expounds the process of intrusion detection based on the danger theory. The model realizes the distributed deployment, and there is no need to arrange an instance at each sensor node. In addition, sensor nodes trigger danger signals according to their own environmental information, and do not need to communicate with other nodes, which saves resources. When danger is perceived, the model acquires the global knowledge through node cooperation, and can perform more accurate real-time intrusion detection. In this paper, the performance of the model is analyzed including complexity and efficiency, and experimental results show that the model has good detection performance and reduces energy consumption.

• 근관절건강학회지
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• 제26권1호
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• pp.27-34
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• 2019

Purpose: This study was done to analyze nursing assessment and nursing care for pain in the electronic nursing records for the elderly patients with abdominal pain visiting the Emergency Medical Center. Methods: This study is a descriptive study based on nursing records from January to December 2015. A total of 1155 records for elderly patients with abdominal pain were gathered. Results: The mean age of elderly patients whose records were analyzed was 75.2 years. Analysis of nursing records regarding pain management showed that semi-urgent severity (93.7%), direct emergency room visits (58%), and 6.01 hours of emergency room stay (6.01 hours)were the most frequently documented characteristics of the elderly patients with pain complaints. Recording time of nursing assessment for abdominal patients was 1.01 hour; the average pain intensity was 3.97. The mostly used nursing intervention for abdominal pain was medication (65.1%). There was no record of non-pharmacological pain nursing interventions. Conclusion: The results of this study showed that improving knowledge and nursing practice for pain management is much of necessity. In particular, development of the non-pharmacological nursing interventions for pain is needed. Further research is also imperative to develop and evaluate record systems for pain management that can be used in the emergency room.

• The Plant Pathology Journal
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• 제34권6호
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• pp.459-469
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• 2018

Plants and microorganisms (microbes) use information from chemicals such as volatile compounds to understand their environments. Proficiency in sensing and responding to these infochemicals increases an organism's ecological competence and ability to survive in competitive environments, particularly with regard to plant-pathogen interactions. Plants and microbes acquired the ability to sense and respond to biogenic volatiles during their evolutionary history. However, these signals can only be interpreted by humans through the use of state-of the-art technologies. Newly-developed tools allow microbe-induced plant volatiles to be detected in a rapid, precise, and non-invasive manner to diagnose plant diseases. Beside disease diagnosis, volatile compounds may also be valuable in improving crop productivity in sustainable agriculture. Bacterial volatile compounds (BVCs) have potential for use as a novel plant growth stimulant or as improver of fertilizer efficiency. BVCs can also elicit plant innate immunity against insect pests and microbial pathogens. Research is needed to expand our knowledge of BVCs and to produce BVC-based formulations that can be used practically in the field. Formulation possibilities include encapsulation and sol-gel matrices, which can be used in attract and kill formulations, chemigation, and seed priming. Exploitation of biogenic volatiles will facilitate the development of smart integrated plant management systems for disease control and productivity improvement.

• 실천공학교육논문지
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• 제9권2호
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• pp.119-124
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• 2017

사출성형 시 유압실린더의 힘으로 스크류가 앞으로 전진하는 동안, 금형의 러너시스템은 성형품 형상의 캐비티 내를 충진시키는 유로로 용융된 수지의 충진, 패킹과정에 관계되며, 이는 스프루(sprue), 러너(runner), 게이트(gate)에 의해서 성형품의 외관, 수지의 물성, 치수 정밀도 및 성형 사이클 등에 큰 영향을 준다. 러너, 게이트 설계가 잘못된 피드시스템은 다양한 성형불량을 일으키므로 이를 방지할 수 있는 최적의 러너밸런스를 유지하는 것이 중요하다. 사출금형을 제작하는 업체에서 응용할 수 있는 실무적인 금형기술 지식을 향상시키기 위해서 기술적인 애로분야에 대한 기술지도를 바탕으로 금형기술 과정의 훈련모델을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4555-4571
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• 2019

Due to increasing interest in distributed agile software development, there is a need to systematically review the literature on challenges encountered in the agile software development environment. Using the Systematic Literature Review (SLR) approach, 32 relevant publications, dated between 2013 and 2018 were selected from four electronic databases. Data from these publications were extracted to identify the key challenges across the system development life cycle (SDLC) phases, which essentially are short phases in each agile-based iteration. 5 types of key challenges were identified as impacting the SDLC phases; these challenges are Communication, Coordination, Cooperation, Collaboration and Control. In the context of the SLDC phases, the Communication challenge was discussed the most often (79 times, 33%). The least discussed challenges were Cooperation and Collaboration (26 times, 11% each). The 5 challenges occur because of distances which occur in distributed environment. This SLR identified 4 types of distances which contribute to the occurrence of these key challenges - physical, temporal, social-cultural and knowledge/experience. Of the 32 publications, only 4 included research which proposed new solutions to address challenges in agile distributed software development. The authors of this article believe that the findings in this SLR are a resource for future research work to deepen the understanding of and to develop additional solutions to address the challenges in distributed agile software development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.