• Journal of Forest and Environmental Science
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• v.36 no.2
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• pp.113-121
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• 2020

This study was carried out in degraded and non-degraded community forests (CF) in the Terai region of Kanchanpur district, Nepal. A total of 63 concentric sample plots each of 500 ㎡ was laid in the inventory for estimating above and below-ground biomass of forests by using systematic random sampling with a sampling intensity of 0.5%. Mallotus philippinensis and Shorea robusta were the most dominant species in degraded and non-degraded CF accounting Importance Value Index (I.V.I) of 97.16 and 178.49, respectively. Above-ground tree biomass carbon in degraded and non-degraded community forests was 74.64±16.34 t ha^-1 and 163.12±20.23 t ha^-1, respectively. Soil carbon sequestration in degraded and non-degraded community forests was 42.55±3.10 t ha^-1 and 54.21±3.59 t ha^-1, respectively. Hence, the estimated total carbon stock was 152.68±22.95 t ha^-1 and 301.08±27.07 t ha^-1 in degraded and non-degraded community forests, respectively. It was found that the carbon sequestration in the non-degraded community forest was 1.97 times higher than in the degraded community forest. CO₂ equivalent in degraded and non-degraded community forests was 553 t ha^-1 and 1105 t ha^-1, respectively. Statistical analysis showed a significant difference between degraded and non-degraded community forests in terms of its total biomass and carbon sequestration potential (p<0.05). Studies indicate that the community forest has huge potential and can reward economic benefits from carbon trading to benefit from the REDD⁺/CDM mechanism by promoting the sustainable conservation of community forests.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.39-46
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• 2020

The utilization of artificial intelligence (AI) in the engagement has been a key research topic in the defense field during the last decade. To pursue this utilization, it is imperative to acquire a realistic simulation to train an AI engagement agent with a synthetic, but realistic field. This paper is a case study of training an AI agent to operate with a hardware realism in the air-warfare dog-fighting. Particularly, this paper models the pursuit of an opponent in the dog-fighting setting with a gun-only engagement. In this context, the AI agent requires to make a decision on the pursuit style and intensity. We developed a realistic hardware simulator and trained the agent with a reinforcement learning. Our training shows a success resulting in a lead pursuit with a decreased engagement time and a high reward.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.97-106
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• 2008

Quality of Protection(QoP) provides a standard that can evaluate networks offering protection. Also, QoP estimates stability of the system by quantifying intensity of the security. Security should be established based on the circumstance which applied to appropriate level, and this should chose a security policy which fit to propose of network because it is not always proportioned that between stability of security mechanism which is used at network and performance which has to be supported by system. With evolving wireless networks, a variety of security services are defined for providing secure wireless network services. In this paper, we propose a new QoP model which makes up for weak points of existing QoP model to choose an appropriate security policy for wireless network. Proposed new QoP model use objectively organized HVM by Flow-based Abnormal Traffic Detection Algorithm for constructing Utility function and relative weight for constructing Total reward function.