The trailing suction hopper dredger(TSHDs) has a typical working condition of opening the bottom mud door for throwing mud in dredging operation. During such mud discharge operations, the vessel's movement and the...
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A design method for optimizing the shape and size of concrete pumping pipes is proposed to address practical engineering challenges. Initially, various cross-sectional shapes for the pipeline were designed to improve ...
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Defect detection models have been widely applied in industrial edge platform. However, in the field of aluminum material defect detection still have some challenges: (i) limited availability of aluminum material defec...
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ISBN:
(纸本)9798350388640;9798350388633
Defect detection models have been widely applied in industrial edge platform. However, in the field of aluminum material defect detection still have some challenges: (i) limited availability of aluminum material defect datasets and (ii) the high computational cost associated with large-scale models. To address these challenges, we propose a novel lightweight model for aluminum material defect detection. For the first challenge, we propose Positive Sample Augmentation(PSA) to effectively increase the number of samples. Additionally, we employ model pruning, knowledge distillation and model quantization to reduce inference latency and model size. We have successfully deployed this solution on real-world industrial platforms, double the running speed and reduce approximately 75% hardware costs, thereby validating the feasibility of our approach.
In this paper an innovative thermal management system is designed to make hydrogen-based propulsion systems more efficient. Hereby the thermal waste heat of drive train components is used for preheating cryogenic hydr...
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We propose an algorithm to detect wormhole attacks based on the Address Resolution Protocol (ARP) for mobile networks which use On-demand Distance Vector (AODV) routing protocol. This novel approach differs from all e...
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A wireless vibration sensor based on surface acoustic wave (SAW) devices as the deformation sensitive elements is a prospective type of sensor for application in different fields of the industry due to the possibility...
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This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to te...
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The transformation to cyber resilience (CR) is a prioritized goal for most organisations world wide, as they are facing highly sophisticated adversaries with increasing resources, experience, and (insider) intelligenc...
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ISBN:
(纸本)9798350375367
The transformation to cyber resilience (CR) is a prioritized goal for most organisations world wide, as they are facing highly sophisticated adversaries with increasing resources, experience, and (insider) intelligence. Threat detection, a key pillar to improve CR, with a traditional focus on network activities, however, has not symmetrically evolved to cover application-level threat detection equally well, rendering it largely a blind spot. In this paper, we outline the lack of methodology to application-level detection engineering as a potential cause. Moreover, we position application intelligence as a key enabler to allow for high-quality, low-noise threat signatures, and sketch out an inclusive detection engineering method. Our contributions ultimately ask software features to inherently support self-awareness and -reporting on suspicious behaviours, thereby rendering threat-detection systems able to rely on the application and user context for determining cyberattacks.
Previous prefetching schemes have been found to be very effective at enhancing the performance of computers. However, speculative prefetching requests can have negative effects on computers, such as increased memory b...
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Previous prefetching schemes have been found to be very effective at enhancing the performance of computers. However, speculative prefetching requests can have negative effects on computers, such as increased memory bandwidth consumption and cache pollution. To address the deficiencies of previous prefetching schemes, we propose the Reinforcement Learning Based Offset Prefetching Scheme (RLOP), an offset prefetching scheme based on reinforcement learning. As with previous offset prefetching schemes, RLOP evaluates multiple offsets and enables offsets that qualify to issue prefetching requests. RLOP, however, selects appropriate prefetch offsets through reinforcement learning, and the reinforcement learning reward scheme determines the goal of the prefetcher;we divide the rewards into four different rewards-accurate and timely prefetch, accurate but delayed prefetch, inaccurate prefetch, and no prefetch operation-and by increasing or decreasing the reward value, we facilitate or inhibit RLOP from future environments to collect such rewards, which enables or inhibits RLOP from collecting such rewards, which enables We evaluated and contrasted RLOP with various advanced data prefetchers and demonstrated that our scheme resulted in a 25.26% increase in system performance over systems without data prefetchers and a 3.8% increase over the previous best performing data prefetcher.
In the domain of precision measurement, achieving high precision and extensive dynamic range detection for unique vibration signals is not readily accomplished by relying on a single MEMS accelerometer. In this paper,...
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