• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.193-200
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• 2022

We conducted a case study to determine the optimal model parameters and predictors of Long Short-Term Memory (LSTM) for the reproduction of dissolved oxygen (DO) concentration in Jinhae Bay. The model parameter case study indicated the lowest accuracy when the Hidden node=10, Epoch=100. This was caused by underfitting of machine learning. The accuracy increased as the Hidden node and Epoch increased. The accuracy was the highest when the Hidden node=80 and Epoch=100 with R²=0.99. In the bottom DO reproduction of Step 1 of the predictors case study, accuracy was highest when the water temperature was used as a predictor with R²=0.81. In Step 2, The R² value increased up to 0.92 when the water temperature and SiO₂ were used as a predictor. This was caused by a high correlation between the bottom DO and SiO₂ concentrations. Consequently, we determined the optimal model parameters and predictors of LSTM for the reproduction of DO concentration in Jinhae Bay.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.79-85
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• 2023

Recently, in the semiconductor process, large companies are seeking process changes from memory semiconductors to the foundry due to the increase in demand due to the 4th industry. industry is expanding. The characteristics of special gases and precursors, which are raw materials used to produce these semiconductor chips, are toxic, pyrophoric, inflammable, and corrosive. These semiconductor raw materials are operated in a closed system and do not leak to the outside during normal times, but when leaked, they spread to the inside of the gas box, and when proper ventilation is not provided inside the gas box, they spread to the outside, causing fires, explosions, or toxic substances. It can lead to major accidents such as leakage. Recently, there have been cases of accidents in which hazardous materials leaked from the closed system of the semi conductor process and spread to the inside and outside of the gas box. . In this study, we propose preventive measures based on the case of an accident in which raw material leaked from the VCR fitting, which is the connection part of the semiconductor raw material transfer pipe, and spread to the outside of the gas box.

• Journal of Pharmacopuncture
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• v.22 no.4
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• pp.231-238
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• 2019

Objectives: Alzheimer's disease (AD), an overwhelming neurodegenerative disease, has deleterious effects on the brain that consequently causes memory loss and language impairment. This study was intended to investigate the neuroprotective activity of the two essential oils (EOs) from Iranian Pistacia khinjuk (PK) leaves and Allium sativum (AS) cloves against β-Amyloid 25-35 (Aβ25-35) induced elevation of cholinesterase enzymes in AD. Methods: The EOs of PK (PKEO) and AS (ASEO) were prepared and analyzed in terms of extraction yield, phenolic content, and cholinergic markers in vitro. Moreover, both were administered orally to adult male Wistar rats at concentrations of 1, 2, and 3%. The inhibitory potential of PKEO and ASEO was compared with Donepezil (0.75 mg/kg) against the high activities of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. Results: PKEO reached an inhibition rate of 83.6% and 81.4% against AChE and BChE, respectively. ASEO had lower anti-cholinesterase activity (65.4% and 31.5% for the inhibition AChE and BChE). PKEO was found to have more phenolic content than ASEO. A significantly positive correlation was observed between the total phenolics and anti-cholinesterase potential. In rats, both EOs decreased the enzyme activity in a concentration-dependent manner. As compared with Donepezil, the significant difference in the AChE and BChE inhibition occurred as rats were treated with PKEO 3% (p < 0.05). Conclusion: It could be concluded that PKEO and ASEO are potent inhibitors of AChE and BChE in rats that hold promise to be used for the treatment of AD.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.19-28
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• 2011

Objective: The aim of this study is to reclassify human errors and to develop hands-on tools to apply the new classification for preventing human error accidents in highway construction site. Background: The main cause of accidents in highway construction was reported as the carelessness of workers. However, such diagnosis could not help us operationally prevent accidents in real workplace. Method: The accidents in highway construction were reanalyzed and the causes of human error were reclassified in order to educate and improve the awareness of human error in highway construction. Field survey and interview with safety managers and workers were conducted to find the causal relationship between the actual accidents and the human errors. Results: The most frequently observed human errors in highway construction were classified into six categories such as mis-perception, distraction, memory fail, slip, cognition error and mis-judgment. In order to provide hands-on tools to increase the awareness of human error in construction field, the human error checklist and card sorting diary were developed. Especially, the card sorting diary was designed to increase the ability in human error inspection of safety manager at construction site. Moreover, posters were developed based on actual accident cases. Conclusion: We suggested that the improved awareness and analytical report on checklist, card sorting diary and posters for construction field could collectively prevent the accident. Application: The classification of human error, hands-on tools and posters can be directly applicable on highway construction site. This analytical and collective approach preventing human error-related accident could be extended to other construction workplaces.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.365-375
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• 2019

A PLC (Programmable Logic Controller) is a highly-reliable industrial digital computer which supports real-time embedded control applications for safety-critical control systems. Real-time operating systems such as uC/OS have been used for PLCs and must meet real-time constraints. As PLCs have been widely used for industrial control systems and connected to the Internet, they have been becoming a main target of cyberattacks. In this paper, we propose an execution code sanitizer to enhance the security of PLC systems. The proposed sanitizer analyzes PLC programs developed by an IDE before downloading the program to a target PLC, and mitigates security vulnerabilities of the program. Our sanitizer can detect vulnerable function calls and illegal memory accesses in development of PLC programs using a database of vulnerable functions as well as the other database of code patterns related to pointer misuses. Based on these DBs, it detects and removes abnormal use patterns of pointer variables and existence of vulnerable functions shown in the call graph of the target executable code. We have implemented the proposed technique and verified its effectiveness through experiments.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.35-43
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• 2019

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1225-1236
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• 2020

Fuzzing is an automated software testing methodology that dynamically tests the security of software by inputting randomly generated input values outside of the expected range. KISA is releasing open source for standard cryptographic algorithms, and many crypto module developers are developing crypto modules using this source code. If there is a vulnerability in the open source code, the cryptographic library referring to it has a potential vulnerability, which may lead to a security accident that causes enormous losses in the future. Therefore, in this study, an appropriate security policy was established to verify the safety of block cipher source codes such as SEED, HIGHT, and ARIA, and the safety was verified using differential fuzzing. Finally, a total of 45 vulnerabilities were found in the memory bug items and error handling items, and a vulnerability improvement plan to solve them is proposed.

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.178-192
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• 2006

The buffer overflow attack is the single most dominant and lethal form of security exploits as evidenced by recent worm outbreaks such as Code Red and SQL Stammer. In this paper, we propose microarchitectural techniques that can detect and recover from such malicious code attacks. The idea is that the buffer overflow attacks usually exhibit abnormal behaviors in the system. This kind of unusual signs can be easily detected by checking the safety of memory references at runtime, avoiding the potential data or control corruptions made by such attacks. Both the hardware cost and the performance penalty of enforcing the safety guards are negligible. In addition, we propose a more aggressive technique called corruption recovery buffer (CRB), which can further increase the level of security. Combined with the safety guards, the CRB can be used to save suspicious writes made by an attack and can restore the original architecture state before the attack. By performing detailed execution-driven simulations on the programs selected from SPEC CPU2000 benchmark, we evaluate the effectiveness of the proposed microarchitectural techniques. Experimental data shows that enforcing a single safety guard can reduce the number of system failures substantially by protecting the stack against return address corruptions made by the attacks. Furthermore, a small 1KB CRB can nullify additional data corruptions made by stack smashing attacks with only less than 2% performance penalty.