Currently, the disassembly of retired electric vehicle battery packs relies on manpower and results in high cost, low efficiency, and poor stability. With the development of artificial intelligence, automated disassembly is an efficient method to largely reduce even completely replace human disassembly. However, the various kinds of battery packs and the uncertainty on their retired numbers and types lead to frequent changes of their disassembly processes. It is necessary to provide a method that can integrate valuable disassembly knowledge to enable automated disassembly. Thus, this study proposes an automated disassembly-oriented knowledge graph for retired battery packs which considers the properties of subassemblies (entities) and explicit physical connections/implicit associations among subassemblies (relations). A large amount of unstructured data exists regarding battery packs, such as product manuals and maintenance records, whereas the knowledge that can be available to guide the disassembly process is dispersed and sparse. To solve this, a candidate entity-based relational triple joint extraction method is developed to efficiently extract the disassembly knowledge, which consists of semantic feature learning, candidate entity recognition, and explicit/implicit relational triple identification. Finally, more than 10,000 sentences collected from multi-source unstructured texts are adopted to verify the proposed method. The experimental results demonstrate that our proposed method achieves an F1-score of 93.99% in candidate entity recognition and an F1-score of 95.6% in triple extraction. Also, the information of disassembly operations, disassembly tools, and subassembly properties can be recommended by the automated disassembly-oriented knowledge graph for retired battery packs.

A bike-sharing system (BSS) is easily unbalanced due to the uncertainty of user demand at each bike station during the day, which appeals for an effective bike reposition solution based on the accurate prediction of user demand. However, there is a discrepancy between the bike pickup/drop-off record (satisfied demand) and the user’s first choice of origin/destination stations (i.e., true user demand) since the BSS cannot capture the unsatisfied user demand (i.e., abandoned rentals and transferred rentals/returns) that occurs at either empty stations (failed rentals) or full stations (failed returns). To efficiently rebalance the BSS, this paper focuses on accurately forecasting the true user demand of the BSS. First, we extract the spatial-temporal features of bike usage and establish a spatio-temporal model for true user demand prediction. Then, an XGBoost-based three-stage prediction approach is proposed to accurately predict the true user demand including the station clustering, the system record rectification, and the true user demand prediction. The real data from the Citi Bike in New York is applied to verify the proposed method and the experimental results demonstrate that the proposed approach outperforms the existing methods.

Disassembly is the first phase to demanufacture end-of-life (EOL) products that are separated into parts/components for recovery. The quality conditions of EOL products are highly uncertain, which would result in some uncertain information during the disassembly process, e.g., the disassembly time and recovering revenue of each subassembly. It is quite challenging to determine the optimal/near-optimal disassembly solutions under uncertain information. This article studies uncertain disassembly planning (UDP) and proposes a self-adaptive learning approach to quickly identify the near-optimal disassembly solutions. First, we model the UDP by extending Petri nets, where not only disassembly operations but also EOL options of each subassembly are represented in the extended Petri Net. Based on the UDP model, we develop the self-adaptive learning approach, which integrates an approximation procedure for estimating uncertain disassembly information, aQ-learning algorithm for training disassembly samples, and a heuristic method for selecting the best disassembly solution. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study and applied to test the proposed self-adaptive learning approach. The experimental results demonstrate that our proposed method can efficiently find a better disassembly solution than the existing disassembly solution within 200 trainings in the case study.

Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a wireless sensor network. When the position of a beacon node is drifted or is spoofed, it becomes an unreliable beacon node, which would lead to a large relocalization deviation of unknown nodes in its neighbor. However, when selecting reliable beacon nodes, most relocalization algorithms only screen either drifting beacon nodes or malicious beacon nodes whose position is drifted or spoofed. This article proposes an algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes. The algorithm is divided into four steps. First, three indicators are introduced, where two are for describing position drifting and one is for describing position spoofing. Second, the entropy method is used to weight the contributions of three indicators. Third, a technique for order preference by similarity to an ideal solution is used to construct a reliability evaluation model. Finally, using the reliability evaluation model select reliable beacon nodes. Experimental results illustrate that the detection accuracy of drifting beacon nodes and malicious beacon nodes of the proposed algorithm is 7.5% and 8.2% higher than that of the state-of-the-art algorithms, respectively.

This paper describes the principles and practice of Declarative Management --- a new approach to the management of database systems. The standard approach to database systems management involves a brittle coupling of interactive operations and procedural scripts. Such ad hoc approach results in incorrect administration, which leads to increased management costs. In the Declarative Management paradigm, a user specifies "what" the desired state is, and the system figures out "how" to get there and stay there. Declarative Management represents a fundamental step towards the goal of a self-managing database system. It also has the potential to significantly lower both administrative error and cost. An initial implementation of Declarative Management has been released with the Microsoft SQL Server 2008 database product, and the paper covers the implementation design as well.

Many applications, such as e-commerce, routinely use copies of data that are not in sync with the database due to heuristic caching strategies used to enhance performance. We study concurrency control for a transactional model that allows update transactions to read out-of-date copies. Each read operation carries a "freshness constraint" that specifies how fresh a copy must be in order to be read. We offer a definition of correctness for this model and present algorithms to ensure several of the most interesting freshness constraints. We outline a serializability-theoretic correctness proof and present the results of a detailed performance study.

Despite the widespread and growing use of asynchronous copies to improve scalability, performance and availability, this practice still lacks a firm semantic foundation. Applications are written with some understanding of which queries can use data that is not entirely current and which copies are "good enough"; however, there are neither explicit requirements nor guarantees. We propose to make this knowledge available to the DBMS through explicit currency and consistency (C&C) constraints in queries and develop techniques so the DBMS can guarantee that the constraints are satisfied. In this paper we describe our model for expressing C&C constraints, define their semantics, and propose SQL syntax. We explain how C&C constraints are enforced in MTCache, our prototype mid-tier database cache, including how constraints and replica update policies are elegantly integrated into the cost-based query optimizer. Consistency constraints are enforced at compile time while currency constraints are enforced at run time by dynamic plans that check the currency of each local replica before use and select sub-plans accordingly. This approach makes optimal use of the cache DBMS while at the same time guaranteeing that applications always get data that is "good enough" for their purpose.

No abstract available.

Recently proposed scalable on-demand streaming protocols have previously been evaluated using a system cost measure termed the "required server bandwidth". For the scalable protocols that provide immediate service to each client when the server is not overloaded, this paper develops simple analytic models to evaluate two client-oriented quality of service metrics, namely (1) the mean client waiting time in systems where clients are willing to wait if a (well-provisioned) server is temporarily overloaded, and (2) the fraction of clients who balk (i.e., leave without receiving their requested media content) in systems where the clients will tolerate no or only very low service delays during a temporary overload. The models include novel approximate MVA techniques that appear to extend the range of applicability of customized AMVA to include questions focussed on state probabilities rather than on mean values, and to systems in which the operating points of interest do not include substantial client queues. For example, the new AMVA models accurately estimate the server bandwidth needed to achieve a balking rate as low as one in ten thousand. The analytic models can easily be applied to determine the server bandwidth needed for a given number of media files, anticipated total client request rate and file access frequencies, and target balking rate or mean wait. Results show that (a) scalable media servers that are configured with the "required server bandwidth" defined in previous work have low mean wait but may have unacceptably high client balking rates (i.e., greater than one in twenty), (b) for high to moderate client load, only a 10 - 50% increase in the previously defined required server bandwidth is needed to achieve a very low balking rate (e.g., one in ten thousand), and (c) media server performance (either mean wait or balking rate) degrades rapidly if the actual client load is more than 10% greater than the anticipated load.