Prevention of Metro Rail Accidents and Incidents in Stations Using RFID Technology

Sahba, Farshid; Sahba, Ramin

doi:10.23919/wac.2018.8430408

Cited by 13 publications

(5 citation statements)

References 30 publications

(28 reference statements)

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“…Moreover, by increasing the trend of using safety-critical mechanical systems such as next-generation composite aircraft, sustainable energy, artificial heart valves, automated drug dispensary equipment, high-speed rail systems, and gas turbines any interruption or alteration in their precise engineering could cause a significant safety problem [22][23][24][25].…”

Section: Safety Health Environmentmentioning

confidence: 99%

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Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

Zarreh¹,

Wan²,

Lee³

et al. 2019

Preprint

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Maintenance is the core function to keep a system running and avoid failure. Total Productive Maintenance (TPM) has broadly utilized maintenance strategy to improve the customer's satisfaction and hence obtain a competitive advancement. However, the complexity of smart manufacturing systems due to the recent advancements, specifically the integration of internet and network systems with traditional manufacturing platforms, has made this function more challenging. The focus of this paper is to explain how cybersecurity could impact the TPM by affecting the overall equipment effectiveness (OEE) in a smart manufacturing system by providing a structured literature survey. First, it provides concerns on principle of TPM regarding cybersecurity in smart manufacturing systems. Then, it highlights the effect of a variety of cyber-physical threats on OEE, as a main key performance indicator of TPM and how differently they can reduce OEE. The countermeasures that could be considered to compensate for the negative impact of a cybersecurity threat on the overall effectiveness of the system also will be discussed. Finally, research gaps and challenges are identified to improve overall equipment effectiveness (OEE) in presence of cybersecurity threats in critical manufacturing industries.

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Section: Safety Health Environmentmentioning

confidence: 99%

“…Eliminate incidents of injuries and accidents caused by cyber threats [20][21][22][23][24][25] Office TPM Addressing traditional IT cybersecurity, securing intellectual properties, data and network [13,26,27] Development management…”

Section: Early/equipment Managementmentioning

confidence: 99%

Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

Zarreh¹,

Wan²,

Lee³

et al. 2019

Preprint

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show abstract

“…Unlike the other sectors such as utility [10], transportation [11], and healthcare [12,13], manufacturing systems are not mature enough towards the cybersecurity threats. There exists only limited research in the field of cybersecurity risk assessment in manufacturing systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Assessment for Cyber Security of Manufacturing Systems: A Game Theory Approach

Zarreh

Wan

Lee

et al. 2019

Procedia Manufacturing

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This paper presents a novel approach using game theory to assess the risk likelihood in manufacturing systems quantifiably. Cybersecurity is a pressing issue in the manufacturing sector. Nevertheless, managing the risk in cybersecurity has become a critical challenge for modern manufacturing enterprises. In risk management thinking, the first step is to identify the risk, then validate it, and lastly, consider responses to the risk. If the risk is below the security risk appetite of the manufacturing system, it could be accepted. However, if it is above the risk appetite, the system should appropriately respond by either avoiding, transferring, or mitigating the risk. The validation of the risk in terms of severity and likelihood of the threat, however, is challenging because the later component is hard to quantify. In this paper, Failure Modes and Effects Analysis (FMEA) method is modified by employing game theory to quantitatively assess the likelihood of cyber-physical security risks. This method utilizes the game theory approach by modeling the rivalry between the attacker and the system as a game and then try to analyze it to find the likelihood of the attacker's action. We first define players of the game, action sets, and the utility function. Major concerns of cyber security issues in the manufacturing area are carefully considered in defining the cost function composed of defense policy, loss in production, and recovery. A linear optimization model is utilized to find a mixed-strategy Nash Equilibrium, which is the probability of choosing any action by the attacker also known as the likelihood of an attack. Numerical experiments are presented to further illustrate the method. Forecasting the attacker's behavior enables us to assess the cybersecurity risk in a manufacturing system and thereby be more prepared with plans of proper responses.

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“…The value of an LBS lies in using information and communication technologies to build a bridge between the real and virtual worlds for users through various intelligent terminal devices (Sahba et al, 2014). With its computing transparency, seamless mobility, universality of information access, and intelligence based on context awareness, an LBS realizes user-centric information services (Sahba et al, 2018;Wen et al, 2012). At present, the main application fields of an LBS include electronic map and navigation, transportation and logistics, daily travel and tourism, location social network recommendation, e-commerce and mobile advertising, entertainment and fitness, and mobile resource management.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Yan-li

2023

International Journal of Information Technologies and Systems Approach

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Aiming at the problems resulting from the fact that the existing point of interest (POI) recommendation methods cannot effectively consider the personalized differences of users' mobile behavior in space and time, the author proposes a personalized POI recommendation method using attention-based time sequence and distance contexts gated recurrent unit (ATSD-GRU). First, the author combined the time sequence and distance context with the GRU to extract useful information from users, effectively alleviating the data sparsity. Second, inspired by the attention mechanism, the author introduced the attention model further into the neural network to capture the user's main mobile behavior intention. Finally, the author studied the ATSD-GRU and trained through Bayesian personalized sorting framework and back propagation algorithm. Experiments imply that the proposed method outperforms the comparison method in terms of the F1 index for any recommended number. When the recommendation list length is 15, the proposed algorithm exhibits an accuracy of 9.23% and a recall rate of 14.65%, both higher than the compared algorithm.

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Prevention of Metro Rail Accidents and Incidents in Stations Using RFID Technology

Cited by 13 publications

References 30 publications

Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

Risk Assessment for Cyber Security of Manufacturing Systems: A Game Theory Approach

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

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