http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Analysis of Mobility and Safety at Intersections for Right Turn
JOKHIO, Sarang(조사랑),CHO, Yong-Bin(조용빈),KIM, Jin-tae(김진태) 대한교통학회 2018 대한교통학회 학술대회지 Vol.78 No.-
While most of the countries comply with the international conventions that prohibit traffic movement (including right turns) on the red signal, the Republic of Korea allows vehicles to turn right on red, exceptionally. The right turn traffic moving on red raises the issue of conflict between cross-directional traffic flow. This study uses VISSIM and SSAM to compare the two methods of right turn treatments: NTOR and RTOR. Further, this study is divided into two parts in terms of ‘mobility’ and ‘safety.’ Following results were obtained when the right turn method transferred from RTOR to NTOR. The delay and the travel time of the right-turn movement increased, while no changes were observed for the cross-direction through movement. The number of potential conflicts decreased in the case of the non-channelized geometric conditions, while no change is found in the case of the channelized geometric condition. In contrast to the general expectation that the conversion from RTOR to NTOR could be positive from the mobility and the safety points of view, the results delivered the opposite with the NTOR.
항만 내 운전자 시거 미확보 환경 극복을 위한 컨테이너 트럭 상충 위험 정보제공 시 교차로 운영 효율성 연구
김동협,JOKHIO SARANG,김진태 대한교통학회 2020 대한교통학회지 Vol.38 No.5
The container port is a “private road” that is not classified as “public road”, and it is difficult to apply safety management measures from the “public road” perspective as it is not divided into roads under the current law. Above all, there is a high risk of accidents caused by failure to secure safety sight distance due to high-loaded containers. This paper proposed a WatchCAT algorithm that can detect and respond to vehicle collision situations for safe passage at the intersection of container port. The proposed algorithm collects vehicle information through V2C technology and uses the collected information to predict potential vehicle collision and provide driver with warnings and provide recommended road speeds to avoid conflicts. The analysis of the effect of the proposed algorithm showed that it was better in terms of efficiency of Intersection than the signal control method and the two-way stop control method in the virtual environment. This research result is expected to be useful in preparing safety management measures for intersections of container port. 항만 컨테이너 야적장은 ‘공도’로 구분되지 않은 ‘사도’로 내부 도로는 현행 법령에서 도로로 구분되지 않아 ‘공도’ 관점의 안전관리 방안이 적용되기 어렵다. 무엇보다 높이 적재된 컨테이너로 인한 안전시거 미확보로 인한 사고의 위험성이 높은 상황이다. 본 논문은 항만 야적장 교차로의 안전한 통행을 위해 차량 위험 상황을 감지하고 대응할 수 있는 WatchCAT 알고리즘을 제안하였다. 제안된 알고리즘은 V2C 기술을 통하여 차량의 정보를 수집하고 수집된 정보를 통해 차량의 잠재상충을 예측하고 운전자에게 위험 경고를 제공하고 상충을 회피할 수 있는 권장 주행속도를 제공한다. 제안된 알고리즘의 효과분석 결과, 가상환경에서 정주기식 신호 제어방식과 양방향 정지 제어방식보다 교차로 효율성 측면에서 우수한 것으로 나타났다. 본 연구결과는 항만 컨테이너 야적장의 교차로 안전관리 대책 마련 시 유용하게 활용될 것으로 기대된다.
Intelligent & Predictive Security Deployment in IOT Environments
Abdul ghani, ansari,Irfana, Memon,Fayyaz, Ahmed,Majid Hussain, Memon,Kelash, Kanwar,fareed, Jokhio International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.12
The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.