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.

Borehole Heat Exchanger (BHE) can be installed in different infrastructures to store or extract energy, such as pavement cooling in summer and de-icing in winter. Although there are approaches to estimating the performance of a BHE based on analytical, semi-analytical or numerical simulation methods, predicting the performance of a BHE in the perspective of Machine Learning (ML) is still necessary. In this investigation, four ML algorithms including Linear Regression (LR), Polynomial Regression (PR), Artificial Neural Network (ANN) and Random Forest (RF) were used to explore the underlying relationship between the Heat Extraction Rate (HER) and the influencing factors. In total, the annual HERs of 400 Thermal Performance Tests (TPTs) covering 12 major factors were computed in a validated numerical simulation framework. After providing necessary database, the 4 ML approaches were trained and evaluated. The results showed that the trained PR approach had the best performance. Specifically, the trained PR approach had a Root-Mean-Square Error (RMSE) lower than 1.74$\text{W}{}\cdot {}\text{m}$−1, with the Coefficient of Determination (R2) higher than 0.99. Additionally, the trained approach was compared to an in-situ TPT in Shijiazhuang (China) and two numerical simulated scenarios in Alsace (France). The results showed that the trained PR approach predicted well the in-situ TPT by having 3$\text{W}{}\cdot {}\text{m}$−1 of RMSE, and it predicted correctly the numerical simulated HER of a BHE with 0.09 and 0.72$\text{W}{}\cdot {}\text{m}$−1 of RMSEs for two scenarios with 5 and 1 °C of inlet fluid temperatures. The proposed prediction model can be applied to estimate the performance of a BHE, providing another option to fast evaluate the BHE performance.

Time series data is widely used in scenarios such as supply chain, stock data analysis, and smart manufacturing. A number of time series database systems have been invented to manage and query large volumes of time series data. We observe that the existing benchmarks of time series databases are focused on workloads of complex analysis such as pattern matching and trend prediction whose performance may be highly affected by the data analysis algorithms, instead of the back-end databases. However, in many real applications of time series databases, people are more interested in the performance metrics such as data injection throughput and query processing time. A benchmark is still required to extensively compare the performance of time series databases in such metrics. We introduce such a benchmark called TS-Benchmark which majorly applies a scenario of device monitoring for wind turbines. A DCGAN-based data generation model is proposed to generate large volumes of time series data from some real time series data. The workloads are categorized into three folds: data loading (in batch), streaming data injection, and historical data access (for typical queries). We implement the benchmark and compare four representative time series databases: InfluxDB, TimescaleDB, Druid and OpenTSDB. The results are reported and analyzed.

Plant recognition recently becomes more and more attractive in computer vision and pattern recognition. Although some researchers have proposed several methods, their accuracy is not satisfactory. Therefore, a novel method of plant recognition based on leaf image is proposed in the paper. Both shape and texture features are employed in the proposed method Texture feature is extracted by intersecting cortical model, and shape feature is obtained by the representation of center distance sequence. Support vector machine is employed for the classifier. The leaf image is preprocessed to get better quality for extracting features, and then entropy sequence and center distance sequence are obtained by intersecting cortical model and center distance transform, respectively. Redundant data of entropy sequence vector and center distance are reduced by principal component analysis. Finally, feature vector is imported into the classifier for classification. In order to evaluate the performance, several existing methods are used to compare with the proposed method and three leaf image datasets are taken as test samples. The experimental result shows the proposed method gets the better accuracy of recognition than other methods.

With the rapid pace of the computer and information development, software projects are becoming increasingly complicated. Teamwork and soft skills are especially important in software engineering and computer programming, where projects are general too strenuous for individuals to accomplish and effective teamwork is vital for efficient completion of the projects. However, these soft skills, such as collaboration, communication ability, expressing power, speaking ability, socio-cultural, independent responsibilities and collision resolution, are just very difficult to teach and learn because of the students' passive role in traditional course. The PTBL learning model combines with Web3D technologies to augment the teamwork skills in software engineering. The PTBL model which is based on Problem-Based Learning and Task-Based Learning has been targeted to overcome common limitations of the traditional course for soft skills. This paper also presents two course based on the PTBL learning model, as well as the experimental results obtained from the courses of the two case studies in software engineering course.

Recent sophisticated worms have spread faster than the patch distribution. We propose an active defense framework that can defend against widespread by reacting quickly to quarantine infections. We propose deploying a number of detectors in the VLAN network. Using Monte Carlo simulations, we have shown that containment of future epidemics is possible and the probability of containment is a function of number of detectors, and that with our assumptions for worm scanning and infection speed characteristics, less than 2% detectors are enough to bring this probability very close to zero

This paper discusses how the conventional similarity measure works on the practical medical data set. The similarity measure used was linear combination of the Mahalanobis distance between numerical attributes and the Hamming distance between nominal attributes. We performed clustering experiments on the meningoencephalitis data set using the similarity measure in conjunction with four types of clustering algorithms: single- and complete-linkage agglomerative hierarchical clustering, Ward's method and rough clustering. Usefulness of the similarity measure was evaluated from the following viewpoints: (1) quality of the generated clusters; and (2) clinical reasonability of the attributes used to generate the high-quality clusters. The results show that the best clusters were obtained using Ward's method where the clinically reasonable attributes were selected. It suggests that this similarity measures would be applicable to the medical data sets.

Dataflow dominated embedded systems often use data flow graphs (DFG) as system models. To achieve the desired performance, these systems usually contain a lot of hardware/software components working in parallel. These concurrent and cooperative components result in the contentions for shared resources due to architecture and data dependencies. The approach to solve the contentions can be priority assignments. In this paper we introduce an algorithm which can find out a priority assignment for a given set of components working in parallel with a timing constraint. In addition, the algorithm also provides a fast way to calculate, whether a set of components working in parallel can guarantee a given timing constraint. Hence the algorithm can be applied both in designing phase and implementation phase of hardware/software co-design for embedded systems.

The fundamental concept of rough sets, upper and lower approximations, provide powerful ways of representing uncertain boundary of regions in images. However, there exist a few studies that discuss effectiveness of this concept in the field of medical image processing, where domain knowledge of experts plays a key role in determining boundaries between anatomically meaningful regions of interests (ROIs). This paper discusses how the expert knowledge can be manipulated in medical image segmentation, especially, how can one treat multiple types of anatomical knowledge about a ROI, such as morphology and location, using upper and lower approximations.

This paper presents a method for analyzing time-series laboratory examination databases. The key concept of this method is classification of temporal patterns using multiscale structure matching and a rough set-based clustering method. Multiscale matching enables us to capture similarity between two sequences of examinations from both short-term and long-term points of view. The rough-set based clustering technique is then applied to classify the sequences according to the relative similarity obtained through multiscale matching. In the experiments we show that this hybrid approach can be used to discover interesting temporal patterns hidden in the time-series databases.

With the evolvement of computer technology, both software system and hardware systems have been developed into large-scale and sophisticated infrastructure. To test these systems, traditional static analysis methods have proved inefficient. This paper introduces a method based on online monitoring. It uses the monitor system MS-1 which is a distributed event-driven hybrid monitor system with synchronous clock system. Online monitor program OM combines the control of the monitor system collection of event traces and simple analysis tools to fulfil the online monitor. OM offers the internal states and the dynamic behavior, giving assistance to debug and test the computer systems, both for hardware and software. Our experience proved that this method can alleviate the difficulties of debugging and testing of parallel and distributed computers efficiently.