• Reproductive and Developmental Biology
• /
• v.30 no.3
• /
• pp.169-174
• /
• 2006

To search of a new porcine pheromonal odorants, the comparative molecular similarity indices analysis(CoMSIA) between porcine odorant binding protein(pOBP) as receptor and ligands of green odorants 2-(Cyclohexyloxy)tetrahydrofurane derivatives as substrate molecule were conducted and disscused quantitatively. In the optimized CoMSIA model(I-AI) with chirality($I:\;C_{1'}(R),\;C_2(S)$) in substrate molecules and atom based fit alignment(AE) of the odorants the statistical PLS results showed the best predictability of the binding affinities based on the LOO cross-validated value ${r^2}_{cv.}\;(q^2=0.856)$ and non cross-validated conventional coefficient(${r^2}_{ncv.}=0.964)$). The structural distinctions of the highest active molecules were able to understand from the interaction between pOBP and green odorants in the contour maps with CoMSIA model.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.613-618
• /
• 2022

Research on artificial intelligence based on SNN (Spiking Neural Networks) is drawing attention as a next-generation artificial intelligence that can overcome the limitations of artificial intelligence based on DNN (Deep Neural Networks) that is currently popular. In this paper, we describe the structure of the SNN compiler, a system SW that generate code from SNN description for neuromorphic computing systems. We also introduce the algorithms used for compiler implementation and present experimental results on how the execution time varies in neuromorphic computing systems depending on the the mapping algorithm. The mapping algorithm proposed in the text showed a performance improvement of up to 3.96 times over a random mapping. The results of this study will allow SNNs to be applied in various neuromorphic hardware.

• Journal of Intelligence and Information Systems
• /
• v.8 no.1
• /
• pp.141-157
• /
• 2002

extensible Markup Language (XML) is a new markup language for data exchange on the Internet. In this paper, we propose a language extensible Rule Markup Language (XRML) which is an extension of XML. The implicit rules embedded in the Web pages should be identifiable, interchangeable with structured rule format, and finally accessible by various applications. It is possible to realize by using XRML. In this light, Web based Knowledge Management Systems (KMS) can be integrated with rule-based expert systems. To meet this end, we propose the six design criteria: Expressional Completeness, Relevance Linkability, Polymorphous Consistency, Applicative Universality, Knowledge Integrability and Interoperability. Furthermore, we propose three components such as RIML (Rule Identification Markup Language), RSML (Rule Structure Markup Language) and RTML (Rule Triggering Markup Language), and the Document Type Definition DTD). We have designed the XRML version 0.5 as illustrated above, and developed its prototype named Form/XRML which is an automated form processing for disbursement of the research fund in the Korea Advanced Institute of Science and Technology (KAISI). Since XRML allows both human and software agent to use the rules, there is huge application potential. We expect that XRML can contribute to the progress of Semantic Web platforms making knowledge management and e-commerce more intelligent. Since there are many emerging research groups and vendors who investigate this issue, it will not take long to see XRML commercial products. Matured XRML applications may change the way of designing information and knowledge systems in the near future.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.675-681
• /
• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.3
• /
• pp.545-554
• /
• 2023

In modern industry, the Global Navigation Satellite System(GNSS) is utilized in various fields, where PNT information (P: Positioning, N: Navigation, T: Timing) is always provided and the accurate location estimation based on PNT information is required. In particular, in order to prevent the infection and the spread of the COVID-19 pandemic situation that began in 2019, the precise GNSS technology and various supporting techniques have been used, and, with active quarantine and efforts for the infection spread restrain around the world, we are facing the transition to an endemic situation. In fields of disease and E-health, the location information of users is absolutely necessary to track and monitor infectionous diseases and provide remote medical services, and GNSS plays a leading role in providing the accurate location information. This paper presents investigation results on the up-to-date research trends in which GNSS technologies are employed in the field of disease and E-health, and analyzes the results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.726-741
• /
• 2022

The purpose of this study is to provide instructive theoretical models for art (music) education institutions especially when unpredictable risks, such as pandemics, occur again. Based on the customer behavior theory of the business-to-business-to-customer (B2B2C) platform, and in combination with the technology acceptance model (TAM) and expectation confirmation model (ECM), this study proposes an online education model from the perspective of art education. The framework is based on the three decision-making processes of the customer, and includes the product owner, content owner, and customer area. This paper highlights the factors that influence customers in making decisions when art education institutions are product owners. Regression analysis was introduced to study the factors influencing the expectation confirmation, and the overall fitting testing and six hypotheses testing of 385 effective samples were performed using the structural equation modeling (SEM). The results show that the course-design and after-service positively influenced the expectation confirmation, and the domain image positively influenced the continuance behavior. Negative emotions skipped the mediator (expectation confirmation) and directly exerted a significant negative impact on customers' willingness to continue system usage (continuance behavior). In addition, expectation confirmation positively influenced continuance behavior. The paths of detailed items comprising course-design, after-service, and negative emotion were also analyzed and discussed. In this path analysis, ordinary art learners did not believe that AI partners can play a very good auxiliary role. The findings contribute to the scope of information systems acting as an art education platform academically, and provide effective and theoretical support for the actual operation of art education institutions.

• Journal of Arbitration Studies
• /
• v.28 no.3
• /
• pp.35-53
• /
• 2018

The purpose of this paper is to focus on the development of the arbitration system, such as the establishment of the arbitration industry and expanding the scope of arbitration fields. The solution method of arbitration differs greatly from that of the court's trial process. This can be seen in the way of autonomous conflict resolution. Therefore, the role of arbitrator is a very important function. In this sense, it seems necessary to establish a professional arbitrator system. Now the Arbitration Promotion Act has been enacted and interest in the arbitration industry is also rising. It is necessary to deal effectively with new incidents according to changes in the legal environment internationally. In order to do this, it is imperative to train professional arbitrators. A training plan for arbitration manager to assist this is now under consideration. The coming of the Fourth Industrial Revolution and the growth of artificial intelligence (AI) technology will simply stop the uniform way of determining winners by lawsuits. Even in new companies entering new markets as well as overseas companies, assistance from arbitration experts is indispensable in order to effectively deal with international trade disputes that will develop in the future. In addition to fostering the arbitration industry, it is necessary to train experts in domestic and foreign arbitration and arbitration practitioners to provide high-quality legal services. For these human resource development measures, we will explore the subject and procedural methods. The Arbitrators Association should concentrate on these matters and be cautious when focusing on the training of arbitrators and arbitration managers through the selection process. The Arbitrators Association must strengthen the level of new education (designation / consignment). Measures must be taken in order to grant such procedures as well as subsequent steps.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1463-1478
• /
• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.533-535
• /
• 2021

The differences between this study and previous studies are as follows. First, by building a cloud-based system using IoT technology, the system was built to monitor the status of buildings in real time from anywhere with an internet connection. Second, a model for predicting the future was developed using artificial intelligence (LSTM) and statistical (ARIMA) methods for the measured time series sensor data, and the effectiveness of the proposed prediction model was experimentally verified using a scaled-down building model. Third, a method to analyze the condition of a building more three-dimensionally by visualizing the structural deformation of a building by convergence of multiple sensor data was proposed, and the effectiveness of the proposed method was demonstrated through the case of an actual earthquake-damaged building.

• Journal of Platform Technology
• /
• v.9 no.4
• /
• pp.42-50
• /
• 2021

Recently, the need to introduce smart farms is increasing in order to solve the problems of intensifying competition such as a decrease in rural population due to aging, a decrease in production, and the inflow of foreign agricultural products, and accordingly, the need for education is increasing. This paper is a study on the implementation of an Android-based IoT smart farm for education so that it can be used in a real environment by reducing the farm's smart farm system. To confirm that Android-based education can be applied in a real environment using the IoT smart farm for education, experiments were performed in automatic mode and manual mode using Bluetooth, Wi-Fi, and server/client communication methods. In the automatic mode, the current status can be checked in real time by receiving all data, and in the manual mode, commands are transmitted in real time using the received sensor data and remote control is performed. As a result of the experiment, it was possible to understand the characteristics of each communication method, and it was confirmed that remote monitoring and remote control of the smart farm using the Android App was possible.