Yi Liang

Hebei GEO University

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i-10 index

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Co-Authors

Dongxiao Niu

North China Electric Power University

Haichao Wang

Jilin Jiangji Special Industries Co., Ltd (China)

Yingying Fan

Hebei GEO University

Yu Cai Dong

Southeast University

Wei‐Chiang Hong

Jiangsu Normal University

Ge Fan

Tencent (China)

Keke Wang

Qingdao University

Xiaomin Xu

First Affiliated Hospital of Wenzhou Medical University

Guangqi Liang

North China Electric Power University

Qiaoran Yang

Hebei GEO University

Cooperative Institutions

Chinese Academy of Sciences

North China Electric Power University

Tsinghua University

Guilin University of Technology

Shanghai Jiao Tong University

Zhejiang University

Hebei GEO University

University of Chinese Academy of Sciences

University of Electronic Science and Technology of China

Peking University

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A Standardized-Based Knowledge Distillation Model for Multimodal Emotion Recognition in Conversation

Yihao Li Turdi Tohti Han Dongfang Yi Liang Zicheng Zuo

10.2139/ssrn.4888543

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Citations (0)

The Modeling and Simulation of Vehicle Transmission Watt Loss Based on Principal Component Regression

Applied Mechanics and Materials (2012)

Lei Gong Yu Cai Dong Su Na Cao Yi Liang

According to analyze the Principal Component Regression, and set up a modeling about the watt loss in a certain vehicle transmission, which the modeling result is keeping a line with the real value. Moreover, compared with the comprehensive watt loss modeling which is the popular application currently, and to analyze the two simulation results in the relative error, which can prove the new simulation result is useful and valid.

Watt

Component (thermodynamics)

Principal component regression

Transmission loss

10.4028/www.scientific.net/amm.246-247.1190

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A Green Technological Innovation Efficiency Evaluation of Technology-Based SMEs Based on the Undesirable SBM and the Malmquist Index: A Case of Hebei Province in China

Sustainability (2021)

Yongfang Peng Yingying Fan Yi Liang

Small- and medium-sized technology-based enterprises (technology-based SMEs) are an important part of China’s scientific and technological development. To a certain extent, the green technological innovation level of technology-based SMEs plays a significant role in supporting the overall development of social green innovation. Carrying out research on green technology innovation efficiency evaluations of technology-based SMEs is helpful to find existing problems to provide references for managers. Therefore, this paper proposes an evaluation system based on the undesirable slack based model (SBM) and the Malmquist index model. Firstly, the evaluation index system of the green technological innovation efficiency of technology-based SMEs in Hebei Province was constructed from the perspectives of input and output, in which environmental pollution is included in the evaluation factors of green innovation activities. Then, the undesirable SBM and the Malmquist index model of green technology innovation efficiency evaluation were constructed. Finally, the technological innovation efficiency of technology-based SMEs in Hebei Province in different regions and time nodes was comprehensively calculated and combined with the Malmquist index model to analyze the efficiency changes of technology-based SMEs in Hebei Province over different years. The results show that the overall level of green technological innovation efficiency of technology-based SMEs in Hebei Province is low, and the regional differences in various cities are obvious, but the main trend is rising. The research in this paper can further improve the research results in the field of evaluation of technology-based SMEs and technological innovation efficiency, as well as play an important role in improving the ecological competitiveness and sustainable development capabilities of the products of Hebei’s technology-based SMEs.

Malmquist index

Technology Innovation

Green innovation

Technology gap

10.3390/su131911079

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Citations (20)

Shedding Light on Employee Pay: The Impact on Job Search and Change

SSRN Electronic Journal (2018)

Steven Balsam Yi Liang

We study the impact of the recently mandated pay-ratio disclosure, which requires public companies disclose in their proxy statements CEO pay, the pay of the median employee, and the ratio of the two. While CEO pay has been disclosed in SEC filings since 1935, the disclosure of the median employee’s pay is new, and provides information to the labor market. We use a series of novel data sets to show this disclosure impacts both job search and change. First, we find that job search activity increases with the reported industry-adjusted median pay. Second, in cross-sectional analysis, we confirm that it is the new disclosure driving the results, by documenting stronger effects for (1) firms in which less pay information was available pre-disclosure, (2) settings where changing jobs are less costly, and (3) firms whose median pay disclosures are reported by the media. Finally, we show the median pay disclosure associated with job changes. Our findings suggest that pay-ratio disclosure has real consequences and can affect the labor market.

Proxy (statistics)

Affect

Pay for Performance

Source

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Citations (1)

Reliability Analysis and Preventive Maintenance of Rehabilitation Robots

Reliability Engineering & System Safety (2024)

Liwei Chen Yi Liang Z K Yang Hongyan Dui

10.1016/j.ress.2024.110704

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Process Pattern Based Software Process Framework

Computer Science (2003)

Yi Liang

Process pattern is an excellent method to express software process knowledge, it can express process knowledge in different granularity and realize the non-gap connection of process knowledge, it supports the reuse and persistent improvement of process knowledge. In this paper, we put forward a process Pattern Based Software Process Framework(PB-SPF), its three layer architecture has realized the high abstraction of software process and the separation of its contents, so it have good reusability and adaptability. We can use it as a base for the research of software process, the building and enactment of process model.

Reusability

Separation of concerns

Team software process

Source

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Research on warship equipment software maintenance

Yi Liang

With the popularity of computer in warships equipment, software has become a key factor that influences the combating power of warship equipment. As the most costly and complex phase in software product' s life cycle, software maintenance has a direct and important effect on the quality and reliability of software products. This paper introduces the basic information of warship equipment software maintenance, and analyzes the key phase, the problem and the solvable schema in maintenance process.

Software Maintenance

Maintenance engineering

Package development process

Popularity

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Semantic Understanding is a Growing Classification Method

Yi Liang Turdi Tohti Askar Hamdulla

Although existing text classification methods have achieved SOTA results on most tasks, their generalization ability is inferior to their excellent performance on a single task. For this issue, existing research mainly improves the generalization of text classification models by adding label semantics to model training and allowing models to see label semantic information. However, its generalization ability is still limited by the description and quantity of labels. In this article, we use the method of comparative learning to make the model more focused on understanding text semantics during training. In this way, the text classification model is exempt from the limitations of labels. At the same time, due to the general semantic knowledge learned by the model, the model can be applied to tasks in most NLP fields. We verified the model's generalization ability on 17 different zero-shot tasks, verified the model's cross-domain generalization ability and growth potential on 3 question-answer and 2 reading comprehension tasks, and verified the model's performance on supervised tasks on 12 text categorization tasks. Experimental results show that using comparative learning to learn semantic knowledge frees text classification models from the limitations of classification labels and improves them in different dimensions: 1) robustness, 2) cross-domain generalization ability, and 3) growth potential.

10.2139/ssrn.4450685

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Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

Energies (2017)

Dongxiao Niu Yi Liang Wei‐Chiang Hong

As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA). In this new model, the original wind speed series are first decomposed into a collection of intrinsic mode functions (IMFs) and a residue. Next, the inherent relationship (partial correlation) of the datasets is analyzed, and the results are then used to select the input for the forecasting model. Finally, the GRNN with the FOA to optimize the smoothing factor is used to predict each sub-series. The mean absolute percentage error of the forecasting results in two cases are respectively 8.95% and 9.87%, suggesting that the hybrid approach outperforms the compared models, which provides guidance for future wind speed forecasting.

Combing

Mode (computer interface)

Wind Power Forecasting

Smoothing

10.3390/en10122001

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Citations (45)

The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction

Energies (2017)

Dongxiao Niu Haichao Wang Hanyu Chen Yi Liang

Accurate and stable prediction of icing thickness on transmission lines is of great significance for ensuring the safe operation of the power grid. In order to improve the accuracy and stability of icing prediction, an innovative prediction model based on the generalized regression neural network (GRNN) and the fruit fly optimization algorithm (FOA) is proposed. Firstly, a feature selection method based on the data inconsistency rate (IR) is adopted to select the optimal feature, which aims to reduce redundant input vectors. Then, the fruit FOA is utilized for optimization of smoothing factor for the GRNN. Lastly, the icing forecasting method FOA-IR-GRNN is established. Two cases in different locations and different months are selected to validate the proposed model. The results indicate that the new hybrid FOA-IR-GRNN model presents better accuracy, robustness, and generality in icing forecasting.

Smoothing

Robustness

10.3390/en10122066

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Citations (42)