Search results

The alignment precision of existing methods is limited by the precision of detecting element and worker’s experience, which the parallelism between ball screw and guide way is not guaranteed effectively. Thus, this paper aims to propose a method of detecting ball screw’s alignment error (BSAE) via monitoring the average vibration magnitude induced by rotational frequency of ball screw (VMRFBS).

In this study, the ball screw is simplified as a freely supported beam. A mathematical model of the effect of BSAE on the contact angle of the ball and screw is established. The change of contact angle has effect on the deformation and contact stiffness according to the Hertz contact theory. To improve the accuracy of the experimental results, the VMRFBS are analyzed by using average method, and the average values of the VMRFBS at different BSAEs are calculated by using the least squares method.

The experimental results show that the average VMRFBS increases with the increasing of BSAE under the BSAE from 0 to 0.2 mm, while the other conditions are unchanged.

This method provides an approach to monitor the BSAE and improve the alignment accuracy of machine tools and automation equipment, which has a certain guide for improving the alignment accuracy of ball screw.

To assess and improve the reliability of computerized numerical control (CNC) lathes, it is essential to collect field failure information throughout the products’ life and perform analysis on these data. This paper describes the collection of field failure data, codification of data and establishment of the field failure database for CNC lathes and gives some examples of the kind of analysis possible when sufficient data have been collected and the database has been accrued.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ_max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.

The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ_max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ_max method.

The effectiveness of the proposed FDCNN model with λ_max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.

The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ_max method.

Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.

A novel FDCNN architecture is implemented with appropriate FAL kernel structures.

This study aims to investigate whether public attention influences corporate decisions on environmental disclosure, thereby revealing how society perceives and understands environmental issues and how corporations respond to these expectations.

We selected publicly listed Chinese firms as our sample. An “Environmental Disclosure Greenwashing” (EDG) Index was developed through textual analysis of their annual reports using natural language processing. Financial data were obtained from the CSMAR database, and multivariate regression was used for analysis.

The impact of public attention on EDG primarily manifests as an oversight pressure effect rather than a legitimacy incentive effect. As public attention intensifies, firms tend to adopt more substantial environmental actions instead of merely symbolic environmental disclosures. Formal regulatory frameworks might inadvertently trigger corporate EDG, but public attention can correct the adverse effects possibly introduced by formal regulations. Notably, in firms facing lower institutional pressure, the influence of public attention is more pronounced.

The evidence suggests that public attention reduces corporate EDG. These findings have significant implications for the regulation of environmental disclosures among firms in emerging economies.

The study integrates research in environmental disclosure with the concept of “greenwashing”, unveiling the limitations of the “disclosure as governance” viewpoint. It elucidates the impact of an informal external oversight mechanism (i.e. public attention) on complex corporate environmental disclosure decisions.

The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab under no magnetic field (NMF), harmonic magnetic field (HMF), pulsed magnetic field (PMF) and two types of out-of-phase pulsed magnetic field (OPMF).

A 3-D multiphysical coupling mathematical model is used to evaluate the corresponding physical fields. The coupling issue is addressed using the method of separating step and result inheritance.

The results suggest that the solute deficiency tends to occur in the central part, while the solute-enriched area appears near the fillet in the molten and solidified regions. Applying magnetic field could greatly homogenize the solute field in the two-phase region. The variance of relative segregation level in the solidified cross-section under NMF is 38.1%, while it is 21.9%, 18.6%, 16.4% and 12.4% under OPMF2 (the current phase in the upper coil is ahead of the lower coil), HMF, PMF and OPMF1 (the current phase in the upper coil lags behind the lower coil), respectively, indicating that OPMF1 is more effective to reduce the macrosegregation level.

There are few reports on the solute segregation degree in rectangle slab under magnetic field, especially for magnesium alloy slab. This paper can act a reference to make clear the solute transport behavior and help reduce the macrosegregation level during DC casting.

Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to improve the medication adherence rate. However, most of the existing electronic pillboxes use time-based reminders which may often lead to ineffective reminding if the reminders are triggered at inopportune moments, e.g. user is sleeping or eating.

In this paper, the authors propose an AI-empowered context-aware smart pillbox system. The pillbox system collects real-time sensor data from a smart home environment and analyzes the user’s contextual information through a computational abstract argumentation-based activity classifier.

Based on user’s different contextual states, the smart pillbox will generate reminders at appropriate time and on appropriate devices.

This paper presents a novel context-aware smart pillbox system that uses argumentation-based activity recognition and reminder generation.

The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network.

The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions.

The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages.

The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study.

Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.

The paper aims to explore how the undue state control leads to the weak stock market in China. It analyzes how the undue state control is exerted in some key areas in the Chinese stock market. This paper intends to expand the existing literature in the relationships among law, politics, and economy.

This paper mainly adopts the exploratory method to analyze the undue state influences. Under some circumstances, comparative study and historical explanation are also adopted.

The paper suggests that to create a strong stock market and facilitate the development of the listed companies and the whole economy, the state should first release its control on the stock market.

Various fields are contained in a stock market, in most of which the undue state control can be observed. In this paper, only some key ones are explored. Further research on other fields and if possible more first‐hand data are necessary.

This paper not only offers an answer to concerns on the various misconducts in the inefficient Chinese stock market and helps to realize the possible ways out of such dilemma, but also it offers implications for other emerging economies.

The on‐going debate on the role of common‐law versus civil‐law system in the capital market may have ignored the state involvement. This paper indicates that it is the undue state control rather than the legal system that leads to the weak stock market in China.