• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1218-1226
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• 2008

With the development of semiconductor integrated technology and with the increasing use of multimedia functions in computer, more functions have been implemented as hardware. Nowadays, most microprocessors beyond 32 bits generally implement an integer multiplier as hardware. However, as for a divider, only specific microprocessor implements traditional SRT algorithm as hardware due to complexity of implementation and slow speed. This paper suggested an algorithm that uses a multiplier, 'w bit $\times$ w bit = 2w bit', to process $\frac{N}{D}$ integer division. That is, the reciprocal number D is first calculated, and then multiply dividend N to process integer division. In this paper, when the divisor D is '$D=0.d{\times}2^L$, 0.5 < 0.d < 1.0', approximate value of ' $\frac{1}{D}$', '$1.g{\times}2^{-L}$', which satisfies ' $0.d{\times}1.g=1+e$, $e<2^{-w}$', is defined as over reciprocal number and then an algorithm for over reciprocal number is suggested. This algorithm multiplies over reciprocal number '$01.g{\times}2^{-L}$' by dividend N to process $\frac{N}{D}$ integer division. The algorithm suggested in this paper doesn't require additional revision, because it can calculate correct reciprocal number. In addition, this algorithm uses only multiplier, so additional hardware for division is not required to implement microprocessor. Also, it shows faster speed than the conventional SRT algorithm and performs operation by word unit, accordingly it is more suitable to make compiler than the existing division algorithm. In conclusion, results from this study could be used widely for implementation SOC(System on Chip) and etc. which has been restricted to microprocessor and size of the hardware.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.31-44
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• 2009

The objective of this study was to measure backscattering coefficients of paddy rice using a L-, C-, and X-band scatterometer system with full polarization and various angles during the rice growth period and to relate backscattering coefficients to rice growth parameters. Radar backscattering measurements of paddy rice field using multifrequency (L, C, and X) and full polarization were conducted at an experimental field located in National Academy of Agricultural Science (NAAS), Suwon, Korea. The scatterometer system consists of dual-polarimetric square horn antennas, HP8720D vector network analyzer ($20\;MHz{\sim}20\;GHz$), RF cables, and a personal computer that controls frequency, polarization and data storage. The backscattering coefficients were calculated by applying radar equation for the measured at incidence angles between $20^{\circ}$ and $60^{\circ}$ with $5^{\circ}$ interval for four polarization (HH, VV, HV, VH), respectively. We measured the temporal variations of backscattering coefficients of the rice crop at L-, C-, X-band during a rice growth period. In three bands, VV-polarized backscattering coefficients were higher than hh-polarized backscattering coefficients during rooting stage (mid-June) and HH-polarized backscattering coefficients were higher than VV-, HV/VH-polarized backscattering coefficients after panicle initiation stage (mid-July). Cross polarized backscattering coefficients in X-band increased towards the heading stage (mid-Aug) and thereafter saturated, again increased near the harvesting season. Backscattering coefficients of range at X-band were lower than that of L-, C-band. HH-, VV-polarized ${\sigma}^{\circ}$ steadily increased toward panicle initiation stage and thereafter decreased, and again increased near the harvesting season. We plotted the relationship between backscattering coefficients with L-, C-, X-band and rice growth parameters. Biomass was correlated with L-band hh-polarization at a large incident angle. LAI (Leaf Area Index) was highly correlated with C-band HH- and cross-polarizations. Grain weight was correlated with backscattering coefficients of X-band VV-polarization at a large incidence angle. X-band was sensitive to grain maturity during the post heading stage.

• 기호학연구
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• no.56
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• pp.45-70
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• 2018

Criticism of the postmodern philosophy of the system of representation, which has continued since the Renaissance, is based on a critique of the dichotomy that separates the subjects and objects and the environment from the human being. Interactivity, highlighted in a series of works emerging as postmodern trends in the 1960s, was transmitted to an interactive aspect of digital art in the late 1990s. The key feature of digital art is the possibility of infinite variations reflecting unpredictable changes based on public participation on the spot. In this process, the importance of computer programs is highlighted. Instead of using the existing program as it is, more and more artists are creating and programming their own algorithms or creating unique algorithms through collaborations with programmers. We live in an era of paradigm shift in which programming itself must be considered as a creative act. Simulation technology and VR technology draw attention as a technique to represent the meaning of reality. Simulation technology helps artists create experimental works. In fact, Baudrillard's concept of Simulation defines the other reality that has nothing to do with our reality, rather than a reality that is extremely representative of our reality. His book Simulacra and Simulation refers to the existence of a reality entirely different from the traditional concept of reality. His argument does not concern the problems of right and wrong. There is no metaphysical meaning. Applying the concept of simulation to algorithmic art, the artist models the complex attributes of reality in the digital system. And it aims to build and integrate internal laws that structure and activate the world (specific or individual), that is to say, simulate the world. If the images of the traditional order correspond to the reproduction of the real world, the synthesized images of algorithmic art and simulated space-time are the forms of art that facilitate the experience. The moment of seeing and listening to the work of Ian Cheng presented in this article is a moment of personal experience and the perception is made at that time. It is not a complete and closed process, but a continuous and changing process. It is this active and situational awareness that is required to the audience for the comprehension of post-representation's forms.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.287-310
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• 2022

Companies' interest in developing AI-based intelligent new products is increasing. Recently, the main concern of companies is to innovate customer experience and create new values by developing new products through the effective use of Artificial intelligence technology. However, due to the nature of products based on radical technologies such as artificial intelligence, intelligent products differ from existing products and development methods, so it is clear that there is a limitation to applying the existing development methodology as it is. This study proposes a new research method based on KANO-TOPSIS for the successful development of AI-based intelligent new products by using car voice assistants as an example. Using the KANO model, select and evaluate functions that customers think are necessary for new products, and use the TOPSIS method to derives priorities by finding the importance of functions that customers need. For the analysis, major categories such as vehicle condition check and function control elements, driving-related elements, characteristics of voice assistant itself, infotainment elements, and daily life support elements were selected and customer demand attributes were subdivided. As a result of the analysis, high recognition accuracy should be considered as a top priority in the development of car voice assistants. Infotainment elements that provide customized content based on driver's biometric information and usage habits showed lower priorities than expected, while functions related to driver safety such as vehicle condition notification, driving assistance, and security, also showed as the functions that should be developed preferentially. This study is meaningful in that it presented a new product development methodology suitable for the characteristics of AI-based intelligent new products with innovative characteristics through an excellent model combining KANO and TOPSIS.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1286-1288
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• 2024

The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.2
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• pp.3-12
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• 2011

Purpose: With increasing medical use of radiation and radioactive isotopes, there is a need to better manage the risk of radiation exposure. This study aims to grasp and analyze the individual radiation exposure situations of radiation-related workers in a medical facility by specific job, in order to instill awareness of radiation danger and to assist in safety and radiation exposure management for such workers. Materials and Methods: From January 1, 2010 December 31, 2010, medical practitioners working in the radiation is classified as a regular personal radiation dosimetry, and subsequently one year 540 people managed investigation department to target workers, dose sectional area, working period, identify the job function-related tasks for a deep dose, respectively, the annual average radiation dose were analyzed. Frequency analysis methods include ANOVA was performed. Results: Medical radiation workers in the department an annual radiation dose of Nuclear and 4.57 mSv a was highest, dose zone-specific distribution of nuclear medicine and in the 5.01~19.05 mSv in the high dose area distribution showed departmental radiation four of the annual radiation dose of Nuclear and 7.14 mSv showed the highest radiation dose. More work an average annual radiation dose according to the job function related to the synthesis of Cyclotron to 17.47 mSv work showed the highest radiation dose, Gamma camera Cinema Room 7.24 mSv, PET/CT Cinema Room service is 7.60 mSv, 2.04 mSv in order of intervention high, were analyzed. Working period, according to domain-specific average annual dose of radiation dose from 10 to 14 in oral and maxillofacial radiology practitioners as high as 1.01~3.00 mSv average dose showed the Department of Radiology, 1-4 years, 5-9 years, respectively, 1.01 workers~8.00 mSv in the range of the most high-dose region showed the distribution, nuclear medicine, and the 1-4 years, 5-9 years 3.01~19.05 mSv, respectively, workers of the highest dose showed the distribution of the area in the range of 10 to 14 years, Workers at 15-19 3.01~15.00 mSv, respectively in the range of the high-dose region were distributed. Conclusion: These results suggest that medical radiation workers working in Nuclear Medicine radiation safety management of the majority of the current were carried out in the effectiveness, depending on job characteristics has been found that many differences. However, this requires efforts to minimize radiation exposure, and systematic training for them and for reasonable radiation exposure management system is needed.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.107-118
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• 2016

The advent of 5G mobile communications, which is expected in 2020, will provide many services such as Internet of Things (IoT) and vehicle-to-infra/vehicle/nomadic (V2X) communication. There are many requirements to realizing these services: reduced latency, high data rate and reliability, and real-time service. In particular, a high level of reliability and delay sensitivity with an increased data rate are very important for M2M, IoT, and Factory 4.0. Around the world, 5G standardization organizations have considered these services and grouped them to finally derive the technical requirements and service scenarios. The first scenario is broadcast services that use a high data rate for multiple cases of sporting events or emergencies. The second scenario is as support for e-Health, car reliability, etc.; the third scenario is related to VR games with delay sensitivity and real-time techniques. Recently, these groups have been forming agreements on the requirements for such scenarios and the target level. Various techniques are being studied to satisfy such requirements and are being discussed in the context of software-defined networking (SDN) as the next-generation network architecture. SDN is being used to standardize ONF and basically refers to a structure that separates signals for the control plane from the packets for the data plane. One of the best examples for low latency and high reliability is an intelligent traffic system (ITS) using V2X. Because a car passes a small cell of the 5G network very rapidly, the messages to be delivered in the event of an emergency have to be transported in a very short time. This is a typical example requiring high delay sensitivity. 5G has to support a high reliability and delay sensitivity requirements for V2X in the field of traffic control. For these reasons, V2X is a major application of critical delay. V2X (vehicle-to-infra/vehicle/nomadic) represents all types of communication methods applicable to road and vehicles. It refers to a connected or networked vehicle. V2X can be divided into three kinds of communications. First is the communication between a vehicle and infrastructure (vehicle-to-infrastructure; V2I). Second is the communication between a vehicle and another vehicle (vehicle-to-vehicle; V2V). Third is the communication between a vehicle and mobile equipment (vehicle-to-nomadic devices; V2N). This will be added in the future in various fields. Because the SDN structure is under consideration as the next-generation network architecture, the SDN architecture is significant. However, the centralized architecture of SDN can be considered as an unfavorable structure for delay-sensitive services because a centralized architecture is needed to communicate with many nodes and provide processing power. Therefore, in the case of emergency V2X communications, delay-related control functions require a tree supporting structure. For such a scenario, the architecture of the network processing the vehicle information is a major variable affecting delay. Because it is difficult to meet the desired level of delay sensitivity with a typical fully centralized SDN structure, research on the optimal size of an SDN for processing information is needed. This study examined the SDN architecture considering the V2X emergency delay requirements of a 5G network in the worst-case scenario and performed a system-level simulation on the speed of the car, radius, and cell tier to derive a range of cells for information transfer in SDN network. In the simulation, because 5G provides a sufficiently high data rate, the information for neighboring vehicle support to the car was assumed to be without errors. Furthermore, the 5G small cell was assumed to have a cell radius of 50-100 m, and the maximum speed of the vehicle was considered to be 30-200 km/h in order to examine the network architecture to minimize the delay.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.171-193
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• 2018

LOD(Linked Open Data) cloud is a practical implementation of semantic web. We suggested a new method that provides identity links conveniently in LOD cloud. It also allows changes in LOD to be reflected to searching results without any omissions. LOD provides detail descriptions of entities to public in RDF triple form. RDF triple is composed of subject, predicates, and objects and presents detail description for an entity. Links in LOD cloud, named identity links, are realized by asserting entities of different RDF triples to be identical. Currently, the identity link is provided with creating a link triple explicitly in which associates its subject and object with source and target entities. Link triples are appended to LOD. With identity links, a knowledge achieves from an LOD can be expanded with different knowledge from different LODs. The goal of LOD cloud is providing opportunity of knowledge expansion to users. Appending link triples to LOD, however, has serious difficulties in discovering identity links between entities one by one notwithstanding the enormous scale of LOD. Newly added entities cannot be reflected to searching results until identity links heading for them are serialized and published to LOD cloud. Instead of creating enormous identity links, we propose LOD to prepare its own link policy. The link policy specifies a set of target LODs to link and constraints necessary to discover identity links to entities on target LODs. On searching, it becomes possible to access newly added entities and reflect them to searching results without any omissions by referencing the link policies. Link policy specifies a set of predicate pairs for discovering identity between associated entities in source and target LODs. For the link policy specification, we have suggested a set of vocabularies that conform to RDFS and OWL. Identity between entities is evaluated in accordance with a similarity of the source and the target entities' objects which have been associated with the predicates' pair in the link policy. We implemented a system "Change Acceptable In-Depth Searching System(CAIDS)". With CAIDS, user's searching request starts from depth_0 LOD, i.e. surface searching. Referencing the link policies of LODs, CAIDS proceeds in-depth searching, next LODs of next depths. To supplement identity links derived from the link policies, CAIDS uses explicit link triples as well. Following the identity links, CAIDS's in-depth searching progresses. Content of an entity obtained from depth_0 LOD expands with the contents of entities of other LODs which have been discovered to be identical to depth_0 LOD entity. Expanding content of depth_0 LOD entity without user's cognition of such other LODs is the implementation of knowledge expansion. It is the goal of LOD cloud. The more identity links in LOD cloud, the wider content expansions in LOD cloud. We have suggested a new way to create identity links abundantly and supply them to LOD cloud. Experiments on CAIDS performed against DBpedia LODs of Korea, France, Italy, Spain, and Portugal. They present that CAIDS provides appropriate expansion ratio and inclusion ratio as long as degree of similarity between source and target objects is 0.8 ~ 0.9. Expansion ratio, for each depth, depicts the ratio of the entities discovered at the depth to the entities of depth_0 LOD. For each depth, inclusion ratio illustrates the ratio of the entities discovered only with explicit links to the entities discovered only with link policies. In cases of similarity degrees with under 0.8, expansion becomes excessive and thus contents become distorted. Similarity degree of 0.8 ~ 0.9 provides appropriate amount of RDF triples searched as well. Experiments have evaluated confidence degree of contents which have been expanded in accordance with in-depth searching. Confidence degree of content is directly coupled with identity ratio of an entity, which means the degree of identity to the entity of depth_0 LOD. Identity ratio of an entity is obtained by multiplying source LOD's confidence and source entity's identity ratio. By tracing the identity links in advance, LOD's confidence is evaluated in accordance with the amount of identity links incoming to the entities in the LOD. While evaluating the identity ratio, concept of identity agreement, which means that multiple identity links head to a common entity, has been considered. With the identity agreement concept, experimental results present that identity ratio decreases as depth deepens, but rebounds as the depth deepens more. For each entity, as the number of identity links increases, identity ratio rebounds early and reaches at 1 finally. We found out that more than 8 identity links for each entity would lead users to give their confidence to the contents expanded. Link policy based in-depth searching method, we proposed, is expected to contribute to abundant identity links provisions to LOD cloud.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.