• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.3-16
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• 2008

A Wireless Sensor Network (WSN for short) is a wireless network consisting of distributed small devices which are called sensor nodes or motes. Recently, there has been an extensive research on WSN and also on its security. For secure storage and secure transmission of the sensed information, sensor nodes should be equipped with cryptographic algorithms. Moreover, these algorithms should be efficiently implemented since sensor nodes are highly resource-constrained devices. There are already some existing algorithms applicable to sensor nodes, including public key ciphers such as TinyECC and standard block ciphers such as AES. Stream ciphers, however, are still to be analyzed, since they were only recently standardized in the eSTREAM project. In this paper, we implement over the MicaZ platform nine software-based stream ciphers out of the ten in the second and final phases of the eSTREAM project, and we evaluate their performance. Especially, we apply several optimization techniques to six ciphers including SOSEMANUK, Salsa20 and Rabbit, which have survived after the final phase of the eSTREAM project. We also present the implementation results of hardware-oriented stream ciphers and AES-CFB fur reference. According to our experiment, the encryption speeds of these software-based stream ciphers are in the range of 31-406Kbps, thus most of these ciphers are fairly acceptable fur sensor nodes. In particular, the survivors, SOSEMANUK, Salsa20 and Rabbit, show the throughputs of 406Kbps, 176Kbps and 121Kbps using 70KB, 14KB and 22KB of ROM and 2811B, 799B and 755B of RAM, respectively. From the viewpoint of encryption speed, the performances of these ciphers are much better than that of the software-based AES, which shows the speed of 106Kbps.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.24-36
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• 2021

Countries around the world, including the Republic of Korea, are participating in efforts to preserve biodiversity. Concerning species, in particular, studies that aim to find potential habitats and establish conservation plans by conducting habitat suitability analysis for specific species are actively ongoing. However, few studies on mid- to long-term changes in suitable habitat areas are based on accumulated information. Therefore, this study aimed to analyze the time-series changes in the habitat suitable area and examine the otters' changing pattern (Lutra lutra) designated as Level 1 endangered wildlife in Gangwon-do. The time-series change analysis used the data on otter species' presence points from the 2nd, 3rd, and 4th national natural environment surveys conducted for about 20 years. Moreover, it utilized the land cover map consistent with the survey period to create environmental variables to reflect each survey period's habitat environment. The suitable habitat area analysis used the MaxEnt model that can run based only on the species presence information, and it has been proven to be reliable by previous studies. The study derived the habitat suitability map for otters in each survey period, and it showed a tendency that habitats were distributed around rivers. Comparing the response curves of the environmental variables derived from the modeling identified the characteristics of the habitat favored by otters. The examination of habitats' change by survey period showed that the habitats based on the 2nd National Natural Environment Survey had the widest distribution. The habitats of the 3rd and 4th surveys showed a tendency of decrease in area. Moreover, the study aggregated the analysis results of the three survey periods and analyzed and categorized the habitat's changing pattern. The type of change proposed different conservation plans, such as field surveys, monitoring, protected area establishment, and restoration plan. This study is significant because it produced a comprehensive analysis map that showed the time-series changes of the location and area of the otter habitat and proposed a conservation plan that is necessary according to the type of habitat change by region. We believe that the method proposed in this study and its results can be used as reference data for establishing a habitat conservation and management plan in the future.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.3
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• pp.13-22
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• 2022

In order to improve water quality of the water system contaminated with dyes, biochars prepared using discarded waste resources were applied in this study. Biochars with a large specific surface area were manufactured using street tree pruning products or waste wood, and were applied to remove an organic dye in synthetic water. Biochars were made by pyrolysis of typical street tree porch products (Platanas, Ginkgo, Aak) and waste wood under air-controlled conditions. Methylene blue (MB), which is widely used in phosphofibers, paper, leather, and cotton media, was selected in this study. The adsorption capacity of Platanas for MB was the highest and the q_max value obtained using the Langmuir model equation was 78.47 mg/g. In addition, the adsorption energy (E) (kJ/mol) of MB using the Dubinin-Radushkevich (D-R) model equation was 4.891 kJ/mol which was less than 8 kJ/mol (a criteria distinguishing physical adsorption from chemical adsorption). This result suggests a physical adsorption with weak interactions such as van der Waals force between the biochar and MB. In addition, the physical adsorption may resulted from that Platanas-based biohar has the largest specific surface area and pore volume. The ∆G value obtained through the adsorption experiment according to temperature variation was -3.67 to -7.68, which also suggests a physical adsorption. Considering these adsorption results, the adsorption of MB onto Platanas-based biochar seems to occur through physical adsorption. Overall, it was possible to suggest that adsorption capacity of the biochr prepared from this study was equal to or greater than that of commercial activated carbon reported in other studies.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.3
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• pp.1-9
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• 2022

This study compared and reviewed the recognition determinants by applying the OECMs criteria, focusing on old big trees, plant of natural monument that are natural heritage under the national heritage system of the Cultural Heritage Administration, and the results are as follows. First, among the protected areas designated and managed by government agencies according to each protection purpose, it is necessary to actively introduce new conservation measures, OECMs, to fulfill the Biodiversity strategy for 2030 while the land area is already saturated. Second, the OECMs are geographically defined areas(CBD, 2018), not currently recognized as a protected areas, governed and managed in a way that achieves positived sustained and effective contribution to in situ conservation of biodiversity. Since the selection of term, the scope of application criteria, and the context of interpretation are inevitably different, it is necessary to separately legislate and establish related laws of the OECMs suitable for each country's situation. Third, as a result of reviewing the OECMs criteria for plant of natural monument, the final 58 potential resources were recognized. Important elements among the OECMs criteria are that buffer zones should be spaced apart from designated zones to secure a certain area, and that economic activities through commercial production should not occur and meet biodiversity standards. Among the potential candidates, 23 areas were analyzed to be geographically isolated and independent, such as Forest of Oriental Arborvitae in Do-dong, Daegu, and forest types such as Carstor Aralia of Gungchon-ri, Samcheok and Forest of Common Camellias in Maryang-ri, Seocheon. As a result of reviewing the application of OECMs criteria for plant of natural monument, it was confirmed that the functions as a traditional uses were specialized among the values of biodiversity, and ecosystem services and cultural and spiritual values were inherited through Korea's unique culture of old big trees and Dangsan ritual. In terms of biodiversity criteria, it can be used as an important factor in connecting human and natural ecosystem networks without the discovery of new species.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.13-29
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• 2023

Overcoming the death valley, a period in which resources are depleted and profitability declines or stagnates in the entrepreneurial process, is an essential procedure for success. In this study, we examined how the strategic orientation(technology, customer, competitor orientations) and absorptive capacity(potential, realized capacities) of startups affect the possibility of startups to overcome the challenges of death valley, and also empirically analyzed whether support of open innovation and venture capital has a moderating influence to the overcoming of death valley. The result of this study shows that customer orientation and realized absorptive capacity have a positive influence on overcoming the death valley. In addition, we found that the support of open innovation and venture capital has a moderating effect only in the technology orientation among the three types of strategic orientations. The result of this research emphasizes (1) the need for startups to take a more customer-oriented approach to overcome the death valley. The customer-oriented behavior and strategies of startups are vital for their longterm survival and success if we consider the fact that most of the companies investigated in this study were technology-based startups and only customer orientation showed significant results in overcoming the death valley. The research outcome also shows that (2) implementing innovation in a more open way and securing venture capital funding can make it easier for startups to overcome the death valley. This study has academic significance in that it empirically analyzed the relationship of key factors influencing the overcoming of death valley in a situation where the majority of existing research remains at the conceptual level of discussion or methodology of case study. Furthermore, this research also provides practical implications for the establishment and implementation of effective strategies to confront the challenges of death valley for startups, government and related organizations.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.203-223
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• 2023

Recently, the platform industry is rapidly growing in the global market, and competition is intensifying at the same time. Therefore, in order for domestic platform companies to have global competitiveness in the platform market, it is necessary to study the platform business ecosystem and success factors. However, most of the recent platform-related studies have been theoretical studies on the characteristics of platform business status analysis, platform economy, and indirect network externalities of platforms. Therefore, this study comprehensively analyzed the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzed the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. And based on these factors, an IPA analysis was conducted as a way to propose a success plan to stakeholders in the platform business ecosystem. As a result of the analysis, among the success factors collected through previous studies, manpower, capital, and challenge culture were identified as factors that are relatively well maintained in both importance and satisfaction in Silicon Valley. In the end, it can be seen that the creation of an environment and culture in which Silicon Valley can use it to challenge itself based on excellent human resources and abundant capital contributes the most to the success of Silicon Valley's platform business. On the other hand, although it is of high importance to Silicon Valley's platform corporate ecosystem, the factors that show relatively low satisfaction among stakeholders are 'learning and benchmarking among active companies' and 'strong ties and cooperation between members', and it is analyzed that interest and effort are needed to strengthen these factors in the future. Finally, the systems and policies necessary for market autonomous competition, 'business support service industry', 'name value', and 'spin-off start-up' were important factors in literature research, but the importance and satisfaction of these factors were lowered due to changes in the times and environment. This study has academic implications in that it comprehensively analyzes the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzes the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. In addition, there is another academic implications that importance and satisfaction were simultaneously examined through IPA analysis based on these various extracted factors. As for academic implications, it is meaningful in that it contributed to the formation of the domestic platform ecosystem by providing the government and companies with concrete information on the success factors of the platform business ecosystem and the theoretical grounds for the growth of domestic platform businesses.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.

• Journal of Korean Elementary Science Education
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• v.43 no.3
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• pp.415-432
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• 2024

This study developed and applied the teacher education model and its principles for science classes using Virtual and Augmented Reality (VR/AR) content and analyzed preservice elementary teachers' feedback on the teacher education model and the changes in their perceptions as to the use of VR/AR content. First, existing Technological Pedagogical Content Knowledge (TPACK) teacher education models and prior studies on the use of the VR/AR contents were reviewed to derive the teacher education model to cultivate the VR/AR-TPACK and set the key principles for each of its stages. The developed teacher education model has five stages: exploration, mapping, collaborative design, practice, and reflection. Second, to examine the appropriateness of the model's five stages and principles, we applied it within the regular course of instruction at the university of education, which was attended by 25 preservice elementary teachers. This study collected data from surveys on the perception of the usage of VR/AR contents before and after the course, as well as the group lesson plans prepared by the preservice teachers, and their feedback on the teacher education model. The feedback on the teacher education model and the survey conducted by the preservice teachers before and after the course were analyzed through open coding and categorization. As a result, most preservice teachers expressed positive opinions about the activities and experiences at each stage of the implementation of the teacher education model. Perceptions related to the usage of the VR/AR content changed in three aspects: first, the vague positive perception of the VR/AR content has changed to a positive perception based on specific educational affordance. Second, they recognized the need for preparedness by anticipating potential problems associated with the use of the VR/AR content. Third, they came to view the VR/AR contents as a useful instructional resource that the teachers could use. Based on these results, we discussed the implications for the VR/AR-TPACK teacher education model and assessed the limitations of the research.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.