• International journal of advanced smart convergence
• /
• v.11 no.4
• /
• pp.96-103
• /
• 2022

This paper presents the analysis of increasing the resolution of True-Time-Delay (TTD) by 0.5-bit for phased-array antenna system which is one of the Multiple-Input and Multiple Output (MIMO) technologies. For the analysis, a 5.5-bit True-Time Delay (TTD) integrated circuit is designed and analyzed in terms of beam steering performance. In order to increase the number of effective bits, the designed 5.5-bit TTD uses Single Pole Triple Throw (SP3T) and Double Pole Triple Throw (DP3T) switches, and this method can minimize the circuit area by inserting the minimum time delay of 0.5-bit. Furthermore, the circuit mostly maintains the performance of the circuit with the fully added bits. The idea of adding 0.5-bit is verified by analyzing the relation between the number of bits and array elements. The 5.5-bit TTD is designed using 0.18 ㎛ RF CMOS process and the estimated size of the designed circuit excluding the pad is 0.57×1.53 mm². In contrast to the conventional phase shifter which has distortion of scanning angle known as beam squint phenomenon, the proposed TTD circuit has constant time delays for all states across a wide frequency range of 4 - 20 GHz with minimized power consumption. The minimum time delay is designed to have 1.1 ps and 2.2 ps for the 0.5-bit option and the normal 1-bit option, respectively. A simulation for beam patterns where the 10 phased-array antenna is assumed at 10 GHz confirms that the 0.5-bit concept suppresses the pointing error and the relative power error by up to 1.5 degrees and 80 mW, respectively, compared to the conventional 5-bit TTD circuit.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.361-369
• /
• 2023

Achieving realistic visual quality while maintaining optimal real-time rendering performance is a major challenge in evolving computer graphics and interactive 3D applications. Normal mapping, as a core technology in 3D, has matured through continuous optimization and iteration. Hybrid normal mapping as a new mapping model has also made significant progress and has been applied in the 3D asset production pipeline. This study comprehensively explores the hybrid normal techniques, analyzing Linear Blending, Overlay Blending, Whiteout Blending, UDN Blending, and Reoriented Normal Mapping, and focuses on how the various hybrid normal techniques can be used to achieve rendering performance and visual fidelity. performance and visual fidelity. Under the consideration of computational efficiency, visual coherence, and adaptability in different 3D production scenes, we design comparative experiments to explore the optimal solutions of the hybrid normal techniques by analyzing and researching the code, the performance of different hybrid normal mapping in the engine, and analyzing and comparing the data. The purpose of the research and summary of the hybrid normal technology is to find out the most suitable choice for the mainstream workflow based on the objective reality. Provide an understanding of the hybrid normal mapping technique, so that practitioners can choose how to apply different hybrid normal techniques to the corresponding projects. The purpose of our research and summary of mixed normal technology is to find the most suitable choice for mainstream workflows based on objective reality. We summarized the hybrid normal mapping technology and experimentally obtained the advantages and disadvantages of different technologies, so that practitioners can choose to apply different hybrid normal mapping technologies to corresponding projects in a reasonable manner.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.98-103
• /
• 2023

As technology advanced dramatically in the late 20th century, a new era of science arrived. The emerging era of scientific discovery, variously described as e-Science, cyberscience, and the fourth paradigm, uses technologies required for computation, data curation, analysis, and visualization. The emergence of the fourth research paradigm will have such a huge impact that it will shake the foundations of science, and will also have a huge impact on the role of data-information infrastructure. In the digital age, the roles of data-information professionals are becoming more diverse. As eScience emerges as a sustainable and growing part of research, data-information professionals and centeres are exploring new roles to address the issues that arise from new forms of research. The functions that data-information professionals and centeres can fundamentally provide in the e-Science area are data curation, preservation, access, and metadata. Basically, it involves discovering and using available technical infrastructure and tools, finding relevant data, establishing a data management plan, and developing tools to support research. A further advanced service is archiving and curating relevant data for long-term preservation and integration of datasets and providing curating and data management services as part of a data management plan. Adaptation and change to the new information environment of the 21st century require strong and future-responsive leadership. There is a strong need to effectively respond to future challenges by exploring the role and function of data-information professionals in the future environment. Understanding what types of data-information professionals and skills will be needed in the future is essential to developing the talent that will lead the transformation. The new values and roles of data-information professionals and centers for 21st century researchers in STEAM are discussed.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.386-394
• /
• 2023

This study investigated changes in salivary cortisol, lactic acid, and heart rate along the route during walking exercise in a forest environment for the purpose of reducing stress. Walking exercise in a forest environment was conducted on a Hill Type (Distance: 800m, Average slope 25°, Altitude 112m) and Step Type (Distance: 800m, Average slope 25°, Altitude 114m) routes for 10 female college students in their 20s. The subjects were asked to walk at a speed of 60 bpm. The resulting changes in salivary cortisol, lactate, and average heart rate during exercise were compared and analyzed using Repeated Measurement two-way ANOVA, and the maximum heart rate during exercise and average heart rate at rest were compared and analyzed using paired t-test, and the following results were obtained. First, there was no significant difference in salivary cortisol depending on the type and period of the forest, but it tended to gradually decrease. Second, there was a significant difference in lactic acid depending on the type and period, and it was higher in Step Type. Third, there was a significant difference in the average heart rate during exercise, and it was higher in Step Type. Fourth, there was a significant difference in maximum heart rate during exercise, and it was higher in Step Type. Fifth, there was no significant difference in average heart rate during rest. In summary, walking exercise in a forest environment can be effective for stress reduction for female college students in their 20s, but it appears that forest routes should be selected according to physical strength level, and walking exercise in a forest environment for long periods of time is not recommended. For this purpose, it is suggested that it is appropriate to select the Hill Type route.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.208-216
• /
• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.434-442
• /
• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• International journal of advanced smart convergence
• /
• v.13 no.1
• /
• pp.194-200
• /
• 2024

In the past, antibiotics and antimicrobial substances have been used for the purpose of promoting the growth of livestock or treating livestock, but various problems such as the presence of livestock products or resistant bacteria have emerged. Recently, regulations on the use of antibiotics have been strengthened worldwide, and probiotics are attracting attention as an alternative. Probiotic microorganisms have already been used for human use, such as intestinal abnormal fermentation, diarrhea, and indigestion. In livestock, beneficial microorganisms are increasing in use for the purpose of improving productivity, such as promoting livestock development and preventing diarrhea. Therefore, it is advisable to understand livestock probiotics in deeper and think about effective uses. The role of probiotics in the livestock sector is made with microorganisms themselves, so it is a substance that promotes livestock growth and improves feed efficiency by settling in the intestines of livestock, suppressing the growth of other harmful microorganisms, helping digestion and absorption of ingested feed, and helping to synthesize other nutrients. There is a need for a probiotic that suppresses intestinal bacteria by supplying probiotics used as a means to minimize the effects of stress in livestock management, thereby suppressing disease outbreaks by maintaining beneficial microorganisms and suppressing pathogenic microorganisms. The purpose of this paper is to develop a brand of feed additive probiotics to improve health conditions due to increased feed intake, improve the efficiency of use of feed nutrients, inhibit the decomposition and production of toxic substances, increase immunity, reduce odor in livestock, and improve the environment. We investigated and analyzed feed additive probiotics already on the market, and developed the naming and logo of suitable feed additive probiotic brands in livestock. We hoped that the newly developed product will be used in the field and help solve problems in the livestock field.

• International journal of advanced smart convergence
• /
• v.13 no.3
• /
• pp.287-299
• /
• 2024

Xiaohongshu, a community-centric social media platform, has pioneered a unique e-commerce model known as 'buyer commerce,' leveraging user-generated content (UGC). Distinctively, Xiaohongshu Live Commerce focuses on fostering deep user relationships and providing superior product and information services, crucial for sustained consumer engagement. This study investigates consumer behavior in purchasing health functional foods via Xiaohongshu Live Commerce, aiming to understand the determinants of continuous usage intention. A novel theoretical framework was devised by integrating the Expectation Confirmation Model (ECM) and the Task-Technology Fit (TTF) model. The research model scrutinizes the impact of Xiaohongshu Live Commerce characteristics, such as perceived usefulness and perceived online intimacy, on task-technology fit. Additionally, it examines the moderating role of perceived risk specific to health functional foods and the influence of expectation confirmation on perceived usefulness, online intimacy, and task-technology fit, alongside their effects on satisfaction and continuous usage intention. The findings reveal that expectation confirmation positively influences perceived usefulness, online intimacy, and task-technology fit. Perceived usefulness significantly enhances task-technology fit, while perceived online intimacy and risk do not significantly affect task-technology fit. Moreover, perceived usefulness and intimacy positively impact consumer satisfaction and continuous usage intention, with task-technology fit playing a pivotal role. Perceived risk moderates the relationship between perceived usefulness and task-technology fit. These insights suggest that companies can augment consumer satisfaction and continuous usage intentions by enhancing the perceived usefulness of technology, effectively managing perceived risks, and continually improving user experience

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.253-262
• /
• 2016

This paper proposes a balance-swap method for the dynamic economic load dispatch problem. Based on the premise that all generators shall be operated at valve-points, the proposed algorithm initially sets the maximum generation power at $P_i{\leftarrow}P_i^{max}$. As for generator i with $_{max}c_i$, which is the maximum operating cost $c_i=\frac{F(P_i)-F(P_{iv_k})}{(P_i-P_{iv_k})}$ produced when the generation power of each generator is reduced to the valve-point $v_k$, the algorithm reduces i's generation power down to $P_{iv_k}$, the valve-point operating cost. When ${\Sigma}P_i-P_d$ > 0, it reduces the generation power of a generator with $_{max}c_i$ of $c_i=F(P_i)-F(P_i-1)$ to $P_i{\leftarrow}P_i-1$ so as to restore the equilibrium ${\Sigma}P_i=P_d$. The algorithm subsequently optimizes by employing an adult-step method in which power in the range of $_{min}\{_{max}(P_i-P_i^{min}),\;_{max}(P_i^{max}-P_i)\}$>${\alpha}{\geq}10$ is reduced by 10; a baby step method in which power in the range of 10>${\alpha}{\geq}1$ is reduced by 1; and a swap method for $_{max}[F(P_i)-F(P_i-{\alpha})]$>$_{min}[F(P_j+{\alpha})-F(P_j)]$, $i{\neq}j$ of $P_i=P_i{\pm}{\alpha}$, in which power is swapped to $P_i=P_i-{\alpha}$, $P_j=P_j+{\alpha}$. It finally executes minute swap process for ${\alpha}=\text{0.1, 0.01, 0.001, 0.0001}$. When applied to various experimental cases of the dynamic economic load dispatch problems, the proposed algorithm has proved to maximize economic benefits by significantly reducing the optimal operating cost of the extant Heuristic algorithm.

• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.6
• /
• pp.661-683
• /
• 2001

There is a rapid increase in the use of digital video information in recent years, it becomes more important to manage video databases efficiently. The development of high speed data network and digital techniques has emerged new multimedia applications such as internet broadcasting, Video On Demand(VOD) combined with video data processing and computer. Video database should be construct for searching fast, efficient video be extract the accurate feature information of video with more massive and more complex characteristics. Video database are essential differences between video databases and traditional databases. These differences lead to interesting new issues in searching of video, data modeling. So, cause us to consider new generation method of database, efficient retrieval method of video. In this paper, We propose the construction and generation method of the video database based on contents which is able to accumulate the meaningful structure of video and the prior production information. And by the proposed the construction and generation method of the video database implemented the video database which can produce the new contents for the internet broadcasting centralized on the video database. For this production, We proposed the video indexing method which integrates the annotation-based retrieval and the content-based retrieval in order to extract and retrieval the feature information of the video data using the relationship between the meaningful structure and the prior production information on the process of the video parsing and extracting the representative key frame. We can improve the performance of the video contents retrieval, because the integrated video indexing method is using the content-based metadata type represented in the low level of video and the annotation-based metadata type impressed in the high level which is difficult to extract the feature information of the video at he same time.