The amount of second-hand manufacturing equipment available in the market is increasing, driven by two primary factors. First, with the rise of reconfigurable assembly lines, manufacturers frequently modify their asse...
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ISBN:
(纸本)9798350358513;9798350358520
The amount of second-hand manufacturing equipment available in the market is increasing, driven by two primary factors. First, with the rise of reconfigurable assembly lines, manufacturers frequently modify their assembly processes and sell their surplus equipment. Second, the growing ecological awareness fosters the development of marketplaces for second-hand equipment. While second-hand machines are cheaper, they are also less reliable, and they require more maintenance. This paper studies the impact of buying secondhand equipment on preventive maintenance and machine replacement policies. We model the degradation process of machines as a Gamma process, and we use a Markov decision process (MDP) to capture the relation among different actions and machine states. We consider two approaches to solve the resulting MDP, namely, Q-learning and policy iteration. Our experimental results suggest that the use of second-hand equipment reduces machine replacement and maintenance costs. Our results also show that manufacturers should buy second-hand equipment even when their cost is only slightly lower than the cost of a new machine.
Stress is a psychological or emotional strain that occurs due to adverse experiences in human life. This paper showcases the application of deep learning in detecting stress levels in continuous audio signals in the D...
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In order to improve the prediction accuracy of photovoltaic power generation and reduce the impact of grid-connected operation of photovoltaic power station on the security, stability and economic operation of power s...
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machinelearning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine-learning experiments and assets when building intelligent software systems. Among other...
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ISBN:
(纸本)9781665495905
machinelearning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine-learning experiments and assets when building intelligent software systems. Among others, they allow tracing back system behavior to experiment runs, for instance, when model performance drifts. Unfortunately, despite a surge of these tools, they are not well integrated with traditional software engineering tooling, and no hard empirical data exists on their effectiveness and value for users. We present a short research agenda and early results towards unified and effective software engineering and experiment management software.
The growing significance of online reviews in e-commerce platforms has a profound impact on consumers' purchase decisions. Unfortunately, dishonest sellers exploit this system by engaging in deceptive practices an...
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In the information age, with the increase of data feature dimensions, the performance of intrusion detection system (IDS) in training time and classification accuracy is declining. A large number of researchers have c...
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Human activity recognition plays a vital role in various applications like healthcare, sports, and smart environments. ... With the increase in the use of smart wearables and sensors, the concern for privacy has incre...
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With the gradual formation of a national digital sharing economy and the growing international concern about environmental issues in developing countries, the emergence of a new "smart environmental protection&qu...
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To accelerate the speed of finding new materials and to predict the attributes of materials, machinelearning has emerged as a very useful tool in recent times. Predicting the band gap of a new material plays a signif...
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machinelearning (ML) dataset preprocessing, cleaning, and integration into ML pipelines is often a cumbersome endeavor that is susceptible to bugs and leads to unstructured code from the start. While existing framewo...
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ISBN:
(纸本)9781665481045
machinelearning (ML) dataset preprocessing, cleaning, and integration into ML pipelines is often a cumbersome endeavor that is susceptible to bugs and leads to unstructured code from the start. While existing frameworks for dataset integration often come with an extensive dataset repository, extending these repositories to new datasets is non-trivial due to lack of dataset retrieval, processing and iterator separation. To simplify the process of dataset integration, we present datastack, an open-source framework that minimizes these efforts by providing well-defined interfaces that seamlessly integrate into existing machinelearning frameworks. Inspired by stream processing frameworks such as Flink or Storm, datastack decouples dataset-specific peculiarities such as custom data formats from the framework by introducing byte streams on an interface level. Furthermore, datastack delivers dataset preprocessing functionalities such as stacking, splitting, and merging to alleviate error-prone data processing pipelines.
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