As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose signific...As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.展开更多
An optimal test method for paint is proposed; additionally, the Field and Laboratory Emission Cell （FLEC） method used in Europe is applied as a substitute for the 20 L small chamber method. The emission factors of t...An optimal test method for paint is proposed; additionally, the Field and Laboratory Emission Cell （FLEC） method used in Europe is applied as a substitute for the 20 L small chamber method. The emission factors of total volatile organic compounds （TVOC） and formaldehyde from oil-based paint, emulsion paint, and water-dispersion paint with a coating weight of 300 g/m2, cured for 24/48 hours, were measured using the 20 L small chamber method. The emission rate of TVOC and formaldehyde from all paints began to stabilize after approximately 7 days after 24/48 hours of curing even though Korean standards stipulate that paint should be measured and analyzed after the third day of application. The emission factor of TVOC and formaldehyde from oil-based, emulsion, and water-dispersion paints were also measured using the FLEC method. There was good correlation between the 20 L small chamber method and the FLEC method for oil-based, emulsion, and water-dispersion paint emissions. With the FLEC method, using paints prepared under identical conditions, the emission rate was stable 24 hours after installation of samples because the air flow rate of FLEC is much higher than that of a 20 L small chamber, and the relative cell volume of FLEC is much smaller than that of a 20 L small chamber.展开更多
As crop harvesting becomes more difficult in environments affected by climate change,the application of artificial intelligence technology to crop management through accurate yield prediction is receiving worldwide at...As crop harvesting becomes more difficult in environments affected by climate change,the application of artificial intelligence technology to crop management through accurate yield prediction is receiving worldwide attention.This study proposes a convolutional neural network(CNN)-based transfer learning framework to increase the productivity and improve the economic feasibility of cherry tomatoes(solanum lycopersicum)in South Korea.You-Only-Look-Once 10 Nano(YOLOv10n)is adopted as a CNN-based algorithm.The source model for transfer learning is trained using cherry tomato imagery from the Tomato Plantfactory Dataset,while the target model is trained based on field survey data collected by the National Institute of Horticultural&Herbal Science,Rural Development Administration,Korea.In that process,an image segmentation technique is developed to improve the prediction accuracy,which reduces the root-mean-square deviation of the existing YOLOv10n from 32.3 to 19.8,a 38.7% reduction.Also,the devised economic feasibility analysis method finds the cost of producing cherry tomatoes in South Korea to be 11.12 USD/m^(2),while the maximum revenue can reach 22.44 USD/m^(2).As a result,the proposed transfer learning framework helps general farms where it is difficult to collect big data to use machine learning techniques to predict crop or vegetable production.展开更多
Climate change and unbalanced energy demand and consumption require innovative approaches to the development of sustainable and renewable energy technologies.Phase change materials(PCMs)present exceptional solutions f...Climate change and unbalanced energy demand and consumption require innovative approaches to the development of sustainable and renewable energy technologies.Phase change materials(PCMs)present exceptional solutions for zeroenergy thermal management due to their outstanding energy storage density at an isothermal phase transition.However,the low thermal transport and thermal stability of bulk PCMs,as well as the expensive and complex synthesis of additive materials,hinder their large-scale utilization.In this study,food-waste-derived engineered biochar(FW)is produced via slow pyrolysis to improve the thermal properties of a microencapsulated bio-PCM(B28).The thermal performance of biochar-PCM composites is evaluated based on two biochar preparation systems:varying activation temperatures(carbonized at 400℃ followed by KOH activation at different temperatures(500–800℃))and varying mass ratios between KOH and biochar.The introduction of a low(0.63 wt%)engineered biochar dopant significantly improves the thermal diffusivity of B28 by more than 1.3-fold.The biochar-PCM microcapsule composites present fusion and crystalline isothermal phase transition temperatures of 29.4±0.38℃ and 16.7±0.13℃,respectively.Moreover,the bio-PCM exhibits a highly efficient energy per unit mass of 61.6 kJ kg^(–1),which is 101.7%of the energy storage capacity of bulk B28.Additionally,the composite demonstrates high thermal stability with decomposition occurring above 195℃,thus enabling an increase of>20℃ in the onset decomposition point compared with pristine B28.Further analysis reveals the impact of the KOH/biochar mass ratio on the thermal properties of bio-PCM.Sample FW6PCM,in which the biochar is activated at 600℃ with a KOH/biochar mass ratio of 1,exhibits the highest enthalpy storage capacity.This study suggests a promising strategy for designing highperformance,eco-friendly,and scalable bio-based composite PCMs by overcoming the long-standing bottleneck of microcapsules,which is crucial for advanced thermal management applications such as cooling and green buildings.展开更多
Material selection and production conditions are imperative for determining the functional performances of composite materials.Phase-change composites obtained from phase-change materials(PCMs)and supporting matrices ...Material selection and production conditions are imperative for determining the functional performances of composite materials.Phase-change composites obtained from phase-change materials(PCMs)and supporting matrices exhibit high thermal energy storage density.They are used to overcome the intermittency issues of wind and solar energy,as well as to reduce waste heat dissipation to the environment.However,the large-scale utilization of composite and pristine materials has severe drawbacks,primarily stemming from the complex fabrication routes of the encapsulating agents,leakage,and inadequate thermal stability.In this study,biochar-based phase-change composites were fabricated using vacuum infiltration techniques,and the effects of biomass feedstock and pyrolysis temperature on the performance of the composite were elucidated using different types of biowastes and temperatures.This approach has several advantages,including facile production techniques,low-cost carbon sources,and environmental friendliness.The PCM adsorption ratio of biochars derived from rice husk(RH)and Miscanthus straw linearly correlated with the pyrolysis temperature(550–700℃),while RH700 resulted in a composite with a high enthalpy per unit mass of hexadecane(HXD)in RH700/HXD(250.9 J g^(−1))owing to the high surface area of RH700(74.66 m^(2)g^(−1)).The crystalline temperature increased slightly from 10.7℃ in RH550/HXD to 10.9℃ in RH700/HXD,suggesting improved molecular motion and crystal growth of HXD.Wheat straw biomass pyrolyzed at a low temperature(550℃),displaying a reduced surface area at 700℃(7.35 m^(2)g^(−1))and exhibiting the lowest energy storage density.The latent heat efficiency reached 99.5–100%,where RH700/HXD exhibited 100%efficiency.The composites demonstrated strong leakage resistance at high heating temperatures(60℃,above the melting temperature of HXD),good chemical compatibility between the biochar and HXD,and high durability after 500 thermal cycles.Therefore,the extent of PCM loading and energy storage density improvements primarily depends on the pyrolysis conditions,feedstock used,and pore size distribution of the biochar samples.This research provides insights into the fabrication of phase-change composites and optimization of the carbonization process of different biomasses used for thermal management applications,such as building energy savings.展开更多
The application of phase change materials(PCMs)in building envelopes can help promote energy efficiency due to its high heat capacity.Our study aimed to provide energy and economic insights for deploying PCM to buildi...The application of phase change materials(PCMs)in building envelopes can help promote energy efficiency due to its high heat capacity.Our study aimed to provide energy and economic insights for deploying PCM to buildings in eight different regions of East Asia through a series of energy and economic analysis using computer modelling and simulations.The static payback period(SPP)and dynamic payback(DPP)methods were used to evaluate the economic feasibility of applying a PCM at different melting phase temperatures(20℃,23℃,25℃,27℃ and 29℃).Results show that the proper choice of a PCM melting temperature is a key factor to improve the performance of the PCM applied to buildings.A melting phase temperature of 29℃ achieved the highest economic feasibility in Seoul,Tokyo;Pyongyang;Beijing;and Ulaanbaatar and a melting temperature of 23℃ in Hong Kong had the highest economic feasibility.Overall,the combined economic and energy analysis presented in this study can play an important role in improving the energy and economic feasibility of PCM in buildings.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00351898 and No.RS-2025-02263915)the MOTIE under Training Industrial Security Specialist forHigh-Tech Industry(RS-2024-00415520)supervised by theKorea Institute for Advancement of Technology(KIAT)+1 种基金the MSIT under the ICAN(ICT Challenge and Advanced Network of HRD)program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.
基金Supported by the National Research Foundation of Korea (NRF) by the Korea Government (MEST) (No. 2011-0001031)
文摘An optimal test method for paint is proposed; additionally, the Field and Laboratory Emission Cell （FLEC） method used in Europe is applied as a substitute for the 20 L small chamber method. The emission factors of total volatile organic compounds （TVOC） and formaldehyde from oil-based paint, emulsion paint, and water-dispersion paint with a coating weight of 300 g/m2, cured for 24/48 hours, were measured using the 20 L small chamber method. The emission rate of TVOC and formaldehyde from all paints began to stabilize after approximately 7 days after 24/48 hours of curing even though Korean standards stipulate that paint should be measured and analyzed after the third day of application. The emission factor of TVOC and formaldehyde from oil-based, emulsion, and water-dispersion paints were also measured using the FLEC method. There was good correlation between the 20 L small chamber method and the FLEC method for oil-based, emulsion, and water-dispersion paint emissions. With the FLEC method, using paints prepared under identical conditions, the emission rate was stable 24 hours after installation of samples because the air flow rate of FLEC is much higher than that of a 20 L small chamber, and the relative cell volume of FLEC is much smaller than that of a 20 L small chamber.
基金supported by a Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant No.RS-2023-00239448)the support of the Korea International Cooperation Agency(KOICA).
文摘As crop harvesting becomes more difficult in environments affected by climate change,the application of artificial intelligence technology to crop management through accurate yield prediction is receiving worldwide attention.This study proposes a convolutional neural network(CNN)-based transfer learning framework to increase the productivity and improve the economic feasibility of cherry tomatoes(solanum lycopersicum)in South Korea.You-Only-Look-Once 10 Nano(YOLOv10n)is adopted as a CNN-based algorithm.The source model for transfer learning is trained using cherry tomato imagery from the Tomato Plantfactory Dataset,while the target model is trained based on field survey data collected by the National Institute of Horticultural&Herbal Science,Rural Development Administration,Korea.In that process,an image segmentation technique is developed to improve the prediction accuracy,which reduces the root-mean-square deviation of the existing YOLOv10n from 32.3 to 19.8,a 38.7% reduction.Also,the devised economic feasibility analysis method finds the cost of producing cherry tomatoes in South Korea to be 11.12 USD/m^(2),while the maximum revenue can reach 22.44 USD/m^(2).As a result,the proposed transfer learning framework helps general farms where it is difficult to collect big data to use machine learning techniques to predict crop or vegetable production.
基金supported by the‘BK21 FOUR(Fostering Outstanding Universities for Research)’in 2020[No.4120240114875,Yonsei University Department of Architecture and Architectural Engineering].
文摘Climate change and unbalanced energy demand and consumption require innovative approaches to the development of sustainable and renewable energy technologies.Phase change materials(PCMs)present exceptional solutions for zeroenergy thermal management due to their outstanding energy storage density at an isothermal phase transition.However,the low thermal transport and thermal stability of bulk PCMs,as well as the expensive and complex synthesis of additive materials,hinder their large-scale utilization.In this study,food-waste-derived engineered biochar(FW)is produced via slow pyrolysis to improve the thermal properties of a microencapsulated bio-PCM(B28).The thermal performance of biochar-PCM composites is evaluated based on two biochar preparation systems:varying activation temperatures(carbonized at 400℃ followed by KOH activation at different temperatures(500–800℃))and varying mass ratios between KOH and biochar.The introduction of a low(0.63 wt%)engineered biochar dopant significantly improves the thermal diffusivity of B28 by more than 1.3-fold.The biochar-PCM microcapsule composites present fusion and crystalline isothermal phase transition temperatures of 29.4±0.38℃ and 16.7±0.13℃,respectively.Moreover,the bio-PCM exhibits a highly efficient energy per unit mass of 61.6 kJ kg^(–1),which is 101.7%of the energy storage capacity of bulk B28.Additionally,the composite demonstrates high thermal stability with decomposition occurring above 195℃,thus enabling an increase of>20℃ in the onset decomposition point compared with pristine B28.Further analysis reveals the impact of the KOH/biochar mass ratio on the thermal properties of bio-PCM.Sample FW6PCM,in which the biochar is activated at 600℃ with a KOH/biochar mass ratio of 1,exhibits the highest enthalpy storage capacity.This study suggests a promising strategy for designing highperformance,eco-friendly,and scalable bio-based composite PCMs by overcoming the long-standing bottleneck of microcapsules,which is crucial for advanced thermal management applications such as cooling and green buildings.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIT)(No.2022R1A2C3008559)supported by Yonsei’Eokkaedongmu Project’through the 4th BK21 Graduate School Innovation Support Project funded by the Ministry of Education.
文摘Material selection and production conditions are imperative for determining the functional performances of composite materials.Phase-change composites obtained from phase-change materials(PCMs)and supporting matrices exhibit high thermal energy storage density.They are used to overcome the intermittency issues of wind and solar energy,as well as to reduce waste heat dissipation to the environment.However,the large-scale utilization of composite and pristine materials has severe drawbacks,primarily stemming from the complex fabrication routes of the encapsulating agents,leakage,and inadequate thermal stability.In this study,biochar-based phase-change composites were fabricated using vacuum infiltration techniques,and the effects of biomass feedstock and pyrolysis temperature on the performance of the composite were elucidated using different types of biowastes and temperatures.This approach has several advantages,including facile production techniques,low-cost carbon sources,and environmental friendliness.The PCM adsorption ratio of biochars derived from rice husk(RH)and Miscanthus straw linearly correlated with the pyrolysis temperature(550–700℃),while RH700 resulted in a composite with a high enthalpy per unit mass of hexadecane(HXD)in RH700/HXD(250.9 J g^(−1))owing to the high surface area of RH700(74.66 m^(2)g^(−1)).The crystalline temperature increased slightly from 10.7℃ in RH550/HXD to 10.9℃ in RH700/HXD,suggesting improved molecular motion and crystal growth of HXD.Wheat straw biomass pyrolyzed at a low temperature(550℃),displaying a reduced surface area at 700℃(7.35 m^(2)g^(−1))and exhibiting the lowest energy storage density.The latent heat efficiency reached 99.5–100%,where RH700/HXD exhibited 100%efficiency.The composites demonstrated strong leakage resistance at high heating temperatures(60℃,above the melting temperature of HXD),good chemical compatibility between the biochar and HXD,and high durability after 500 thermal cycles.Therefore,the extent of PCM loading and energy storage density improvements primarily depends on the pyrolysis conditions,feedstock used,and pore size distribution of the biochar samples.This research provides insights into the fabrication of phase-change composites and optimization of the carbonization process of different biomasses used for thermal management applications,such as building energy savings.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science ICT and Future Planning(NRF-2017R1D1A1A09000639)financially supported by Korea Ministry of Environment(MOE)as“Graduate School specialized in Climate Change.”。
文摘The application of phase change materials(PCMs)in building envelopes can help promote energy efficiency due to its high heat capacity.Our study aimed to provide energy and economic insights for deploying PCM to buildings in eight different regions of East Asia through a series of energy and economic analysis using computer modelling and simulations.The static payback period(SPP)and dynamic payback(DPP)methods were used to evaluate the economic feasibility of applying a PCM at different melting phase temperatures(20℃,23℃,25℃,27℃ and 29℃).Results show that the proper choice of a PCM melting temperature is a key factor to improve the performance of the PCM applied to buildings.A melting phase temperature of 29℃ achieved the highest economic feasibility in Seoul,Tokyo;Pyongyang;Beijing;and Ulaanbaatar and a melting temperature of 23℃ in Hong Kong had the highest economic feasibility.Overall,the combined economic and energy analysis presented in this study can play an important role in improving the energy and economic feasibility of PCM in buildings.
