Search results

The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate.

Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system.

The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement.

This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis.

In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

When the controller of a storage system becomes more and more powerful, it sometimes creates new data and stores this data in the system, just like parity information in RAID level 5. We call these phenomena data self‐create. This paper provides a theory about data self‐create which separates data self‐create phenomena into 16 kinds. Three applications are introduced. From a pansystems view, this paper also gives an explanation of data self‐create.

The purpose of this study is to examine how the implementation of edge computing can enhance the progress of the circular economy within supply chains and to address the challenges and best practices associated with this emerging technology.

This study utilized a streamlined evaluation technique that employed Latent Dirichlet Allocation modeling for thorough content analysis. Extensive searches were conducted among prominent publishers, including IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi, utilizing pertinent keywords associated with edge computing, circular economy, sustainability and supply chain. The search process yielded a total of 103 articles, with the keywords being searched specifically within the titles or abstracts of these articles.

There has been a notable rise in the volume of scholarly articles dedicated to edge computing in the circular economy and supply chain management. After conducting a thorough examination of the published papers, three main research themes were identified, focused on technology, optimization and circular economy and sustainability. Edge computing adoption in supply chains results in a more responsive, efficient and agile supply chain, leading to enhanced decision-making capabilities and improved customer satisfaction. However, the adoption also poses challenges, such as data integration, security concerns, device management, connectivity and cost.

This paper offers valuable insights into the research trends of edge computing in the circular economy and supply chains, highlighting its significant role in optimizing supply chain operations and advancing the circular economy by processing and analyzing real time data generated by the internet of Things, sensors and other state-of-the-art tools and devices.

As in real applications several alternating current (AC) currents may be injected to the electronic devices, this study aims to analyze their effects on the lifetime of the solder joints and, consequently, shed the light on these effects at the design phase for other researchers to consider.

In this paper, the authors investigated on current waveform shapes on the performance and reliability of the solder joints in electronic package. Three common and extensively used current shapes in several simulations and experiments were selected to study their effects on the solder joint performance.

The results demonstrate a sever thermal swing and stress fluctuation in the solder joint induced in the case of triangle current type because the critical states lack any relaxation time. In fact, the stress intensification in the solder under application of the triangle current type has been shown to contribute to increasing brittle intermetallic compounds. An accelerated increase of on-state voltage of power semiconductor was also observed in under application of the triangle current type.

The originality of this paper is confirmed.

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

Energy-efficient retrofitting of existing buildings is an inexpensive way of reducing energy consumption and mitigating climate change impacts. The purpose of this study is to examine electricity savings and carbon dioxide (CO₂) emission reduction potentials of energy-efficient retrofit measures for surveyed two large shopping malls in India.

A techno-economic model was developed to estimate the electricity savings achieved due to energy-efficient retrofit measures in shopping malls that were surveyed in 2017. Alternative scenarios were constructed based on capital cost and cost of conserved energy (CCE) value for retrofit measures: cheapest replacement, best available technology and best value for money. The life-cycle electricity and CO₂ emission savings and payback period for end-use retrofit measures were evaluated.

The estimated average electricity savings were around 39–56% for various retrofit measures across all three scenarios while the average CO₂ emission reductions were around 50–125 kt-CO₂. Retrofits to light-emitting diode lights and air conditioners with inverter technology offered more life-cycle electricity savings. Paybacks for most lighting end-use measures were estimated to be within 1.5 years while for most space conditioning end-use measures were between 1 and 4 years.

The primary survey-based comprehensive research makes an exclusive contribution by estimating life-cycle electricity savings and CO₂ emission reductions for energy-efficient retrofit measures of lighting and space cooling end-use appliances for existing shopping malls. The present research methodology can also be deployed in other types of commercial buildings and in residential buildings to estimate electricity savings from energy-efficient retrofit measures.

This study aims to investigate the most effective approach for governments and enterprises to combat desertification by considering the governance cycle. The focus is on understanding how the government can incentivize enterprises to actively engage in desertification combat efforts.

Both the government and the enterprise are treated as rational entities, making strategic choices for joint participation in combating desertification. Recognizing the dynamic nature of the desertification combat area, differential game models are employed to identify the optimal mode for combating desertification.

The findings underscore the significant influence of the governance cycle duration on the selection of desertification combat modes for government and enterprise. A cooperative mode is best suited to a short governance cycle, while an ecological subsidy mode is optimal for a longer cycle. Enhancing governance technology and shortening the governance cycle are conducive to combating desertification. Reducing taxes alone may not be an effective control strategy; rather, the government can better motivate enterprises by adopting tax rate policies aligned with the chosen governance mode.

This research contributes by elucidating the impact mechanism of the government cycle’s length on the desertification combat process. The results may offer valuable insights for governments in formulating strategies to encourage corporate participation in combating desertification and provide theoretical support for selecting optimal desertification combat modes.

This study aims to provide a comprehensive overview of the current state of the art in powder bed fusion (PBF) techniques for additive manufacturing of multiple materials. It reviews the emerging technologies in PBF multimaterial printing and summarizes the latest simulation approaches for modeling them. The topic of “multimaterial PBF techniques” is still very new, undeveloped, and of interest to academia and industry on many levels.

This is a review paper. The study approach was to carefully search for and investigate notable works and peer-reviewed publications concerning multimaterial three-dimensional printing using PBF techniques. The current methodologies, as well as their advantages and disadvantages, are cross-compared through a systematic review.

The results show that the development of multimaterial PBF techniques is still in its infancy as many fundamental “research” questions have yet to be addressed before production. Experimentation has many limitations and is costly; therefore, modeling and simulation can be very helpful and is, of course, possible; however, it is heavily dependent on the material data and computational power, so it needs further development in future studies.

This work investigates the multimaterial PBF techniques and discusses the novel printing methods with practical examples. Our literature survey revealed that the number of accounts on the predictive modeling of stresses and optimizing laser scan strategies in multimaterial PBF is low with a (very) limited range of applications. To facilitate future developments in this direction, the key information of the simulation efforts and the state-of-the-art computational models of multimaterial PBF are provided.

The research aims to examine the impacts of two different types of goal orientation, i.e. leaning goal orientation (LGO) and performance-prove goal orientation (PPGO), on employee knowledge sharing, and whether these relationships are altered by leaders' boundary spanning behavior (BSB).

A sample of 170 employees and 93 leaders were collected from South Korean organizations. Data were analyzed using path analysis.

The results demonstrated that employees with a high LGO are more likely to engage in knowledge sharing, while employees with a high PPGO are less likely to show knowledge sharing. Moreover, the findings showed that highly learning oriented employees tend to engage in knowledge sharing when they are under leaders who show active BSB.

To encourage knowledge sharing between employees, organizations need to promote a mastery-structured work environment to help employees develop a stronger LGO. Also, organizations could benefit from implementing training programs for leaders that promote their BSB.

The research aims to provide a more articulate account for how LGO and PPGO affect employee knowledge sharing in opposite ways. More importantly, this research provides new insights regarding the role of leaders' BSB as a contextual factor in promoting employee knowledge sharing. Using social learning theory as a theoretical framework, this study indicates how employees with a high LGO learn the behaviors of boundary spanning leaders and are more willing to engage in knowledge sharing.

The purpose of this study is to investigate the phenomenon of knowledge transfer between employees and coworkers. That is, when and why employees engage in knowledge seeking or knowledge sabotage when confronted with coworkers with higher relative overqualification.

This study collected survey data from 315 employee-coworker pairs in East China at three-time points.

The results showed that when the cooperative goal interdependence between employee and coworker is high, the perception of coworker’s relative overqualification will cause benign envy of employees, which in turn promote employees to engage in knowledge seeking from coworker. However, when the competitive goal interdependence between employee and coworker is high, the perception of coworker’s relative overqualification will cause malicious envy of employees, which in turn promote employees to engage in knowledge sabotage toward coworker.

This research not only expands the theoretical perspective and outcomes of relative overqualification but also enriches the mechanism of knowledge seeking and knowledge sabotage. Meanwhile, this study also provides practical guidance for enterprises to reduce knowledge sabotage.