Zhile Yang

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Verified

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• Doctor of Philosophy
• Professor at Chinese Academy of Sciences

Significant penetration of renewable generations (RGs) and mass roll-out of plug-in electric vehicles (PEVs) will pay a vital role in delivering the low carbon energy future and low emissions of greenhouse gas (GHG) that are responsible for the global climate change. However, it is of considerable difficulties to precisely forecast the undispatchab...

Batch machining systems are essential for improving productivity and quality, but they consume considerable amounts of energy due to the continuous interaction with machine tools, workpieces, and cutting tools. In contrast to single-piece machining that has a short production cycle, the tool wear impacts in batch machining systems on energy consump...

Large Language Models (LLMs) have been applied in intelligent question answering of housing and construction knowledge, design assistance, analysis and decision-making. This paper uses a combination of literature research and case analysis. Firstly, this paper conducts a literature analysis, sorts out the development process of LLMs, and introduces...

Accurately predicting the remaining useful life (RUL) of machines is vital for assessing machine health and minimizing economic losses resulting from downtime in sensor-equipped machines. However, real-world applications often encounter challenges such as rapid production cycles and unstable network conditions, inevitably leading to significant amo...

The global energy crisis and the pursuit of carbon neutrality have introduced significant challenges to the optimal dispatch of power systems. Despite advancements in optimization techniques, existing methods often struggle to efficiently handle the uncertainties introduced by renewable energy sources and the dynamic behavior of plug-in electric ve...

Public health emergencies influence urban carbon emissions, yet an in-depth understanding of deviations between regional emissions under such emergencies and normal levels is lacking. Inspired by the concept of resilience, we introduce the concept of regional carbon resilience and propose four resilience indicators covering periods during and after...

The charging process of lithium‐ion batteries is necessary for normal operation, and improper lithium‐ion battery charging strategy can cause side reactions, significant temperature rise, performance degradation, and safety concerns. This paper proposes a two‐layer dynamic economic nonlinear model predictive control economic model predictive contro...

The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating u...

The potential intelligence behind advanced machining systems (AMSs) offers positive contributions toward process improvement. Imitation learning (IL) offers an appealing approach to accessing this intelligence by observing demonstrations from skilled technologists. However, existing IL algorithms that implement single policy strategies have yet to...

Global fossil fuel consumption and associated emissions are continuing to increase amid the 2022 energy crisis and environmental pollution and climate change issues are becoming even severer. Aiming at energy saving and emission reduction, in this paper, a new unit commitment model considering electric vehicles and renewable energy integration is e...

Dynamic events, such as machine fault and rush order insertion, are fairly common in the job shop scheduling, which may lead to significant delay in order delivery and low production efficiency. Under such circumstance, it is urgent to consider more perspectives in the scheduling, such as delay time and equipment load rate. In this article, a dynam...

Innovation is a key driver of modern economic and technological development. Correct and equitable identification of innovation is essential for promoting market competitiveness and ensuring the optimal allocation of resources. Existing research on innovation evaluation mainly focuses on qualitative or quantitative evaluation of the results, while...

The health status of equipment is of paramount importance during the operation of nuclear power plants. The occurrence of faults not only leads to significant economic losses but also poses risks of casualties and even major accidents, with unimaginable consequences. This paper proposed a deep learning framework called PT-Informer for fault predict...

The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to track the photovoltaic plan to a greater extent, a real-time charge and discharge power control method based on deep reinforcement learning is proposed. Firstly, the photovoltaic and ener...

With the development of Industry 4.0, there is an increasing demand for industrial digital transformation. As a clean energy source with high efficiency, nuclear power is confronting new challenges of safety and digital and intelligent operational stability. One of these issues is the accurate prediction of the life of nuclear power equipment. In t...

Multi-robot path planning is a vital part of Simultaneous localization and mapping (SLAM) systems. In recent years, numerous studies have been conducted in the field of multi-robot path planning. This paper presents a novel method for multi-robot path planning that addresses the challenge of dynamic environments. This method joints adaptive paramet...

An improved cross-entropy (CE) method assisted with a chaotic operator (CGSCE) is presented for solving the optimal power flow (OPF) problem. The introduction of the chaotic operator helps to enhance the exploration capability of the popular cross-entropy approach while the global best solution is preserved. To handle the constraints in the optimal...

Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the grid (V2G), the fluctuations in the grid can be mitigated, and the benefits of...

The accurate determination of work package size is crucial for cost reduction and achieving efficient mass production in modular construction (MC) projects. However, effectively balancing sizing and cost poses a significant challenge due to the absence of a precise trade-off method. This paper describes an automatic work package sizing (AWPS) metho...

The unit commitment (UC) problem is the first step in power system optimal scheduling and system planning, and ensures the safety, economy, environmental protection and other requirements of the power system simultaneously. However, the UC problem is a mixed integer optimization problem, which usually has the characteristics of high dimension , non...

In this paper, a new method of sodium-ion battery SoC prediction based on recurrent deep forest is proposed. The method uses data that is easy to be measured online, such as voltage, current, voltage and current at the previous moment, as the input characteristics of the model. The predicted value of the SoC is also used for the input of the next m...

With the application of smart meters, more information is available from residential buildings for support heat load forecast. Yet, there is still a lack of an effective method to exploit the value of the high spatial granularity information, particularly for residential communities with high randomness in human behaviors. To fill this gap, this pa...

Recent advancements in computer vision and augmented reality (AR) technology have created a pathway for enhanced human–computer interaction. Despite the potential benefits, the integration of these technologies is not yet common in the industry. Object detection is the foundation of AR systems for inspection, and an efficient object detection model...

The increasing penetration of renewable energy in power systems has been playing an increasingly important role on power system reliability. However, traditional reliability indices of power systems, such as the loss-of-load probability and the expected unserved load, can only reflect the generation capacity adequacy but neglect its flexibility. Fo...

A large proportion of construction accidents are caused by unintentional and unsafe actions and behaviors. It is of significant difficulties and ineffectiveness to monitor unsafe behaviors using conventional manual supervision due to the complex and dynamic working conditions on construction sites. Recently, surveillance videos and computer vision...

Unbalanced data with very few samples for special abnormal conditions frequently occur in actual production processes, which can make accurate monitoring of the process state challenging. This paper proposes a multi-modal few-shot learning method (MMFSL) within a fault diagnosis framework for unbalanced data modelling of industrial bearings. MMFSL...

A Plate-fin heat exchanger (PFHE) is a compact and efficient thermal device, whose performance strongly depends on its structural design. However, the design optimization of a PFHE is a mixed-integer optimization problem with a strong nonlinear characteristic, which presents significant challenges for existing optimization algorithms. Meta-heuristi...

The flexible job shop scheduling problem (FJSP) is of great importance for realistic manufacturing, and the problem has been proven to be NP-hard (non-deterministic polynomial time) because of its high computational complexity. To optimize makespan and critical machine load of FJSP, a discrete improved grey wolf optimization (DIGWO) algorithm is pr...

Adaptive work packaging is paramount in helping reduce dynamic gaps between design and manufacturing in modular construction (MC), particularly in mass customization. However, current work packaging methods fail to automatically extract complex semantic relations among work package elements (e.g., products, tasks, and their dependencies) and dynami...

Earthwork excavator, as an all-terrain and high-efficiency earthwork excavation equipment, has been widely used in earthwork sites. It is very necessary to analyze the work of earthmoving excavator by means of machine vision. In this paper, the action segmentation method based on long video was applied to the analysis and recogniton of the excavato...

Due to the large amount of data generated during modern industry, an urgent need for quickly and effectively data analysis exists to automatically provide accurate diagnosis results, as well as prediction of long-term series containing faults is fast becoming a key instrument. In order to solve the above- mentioned challenge, this paper proposed a...

Due to the considerable number of electric vehicles and the characteristics of energy storage, it is possible for these new energy factors to participate in the operation and regulation of the power system and provide reserve service. In view of this, a multi-objective optimal scheduling model is established, aiming at the economic benefits of elec...

Teaching-learning-based optimization (TLBO) is a powerful metaheuristic algorithm for solving complex optimization problems pertaining to the global optimum. Many TLBO variants have been presented to improve the local optima avoidance capability and to increase the convergence speed. In this study, a modified learner phase and a new gradient-based...

The mass roll-out of electric vehicles substantially contributes to reducing fossil fuel consumption and environmental emissions. Meanwhile, the large fluctuation, strong uncertainty and multiple coupled impact factors of plug-in electric vehicle charging significantly challenge the existing load forecasting approaches. Unlike the conventional load...

Unmanned Aerial Vehicles (UAVs) technology has seen a significant boost in the past ten years and has been widely adopted in entertainment, rescue, intelligent transportation, no-touch delivery, environmental monitoring and other real world applications. However, the ranging limitation due to the shortage of battery energy capacity remains a major...

Grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic-opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a ran...

Construction projects face various constraints in terms of materials, labor, equipment, and documents, which can interrupt the scheduled work. Package‐based constraint management (PCM) is a state‐of‐the‐art graph‐based approach that follows the lean theory to effectively model, monitor, and remove constraints before the commencement of work, ensuri...

Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dyna...

The flexible job shop scheduling problem (FJSP) is an extension of the classical job shop scheduling problem and one of the more well-known NP-hard problems. To get better global optima of the FJSP, a novel hybrid whale optimization algorithm (HWOA) is proposed for solving FJSP, in which minimizing the makespan is considered as the objective. First...

Many optimal design problems in the engineering field are nonlinear, multivariate, mixed integer, multimodal, and constrained. Meta-heuristic approaches have been widely used to solve these complex problems, but most of them are often sensitive to the settings of tuning parameters for different optimization problems, and suffer from premature conve...

Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit para...

The unmanned aerial vehicle (UAV)-assisted wireless power and information system is one of the great choices for energy supplement and information collection of the wireless sensor network (WSN). The ground wireless sensors are operating by the harvested radio frequency (RF) energy from UAVs. The lifetime of the wireless sensor network is affected...

The development of the building energy management systems (BEMS) enable users to intelligently control Heating, Ventilation, Air-conditioning and Cooling (HVAC) systems based on digital information. In order to reduce the power consumption cost of the HVAC system while ensuring user satisfaction, a novel HVAC control system for building system base...

The performance of photovoltaic (PV) cell is affected by the model structure and corresponding parameters. However, these parameters are adjustable and variable, which play an available role in regarding to the efficiency and effectiveness of PV generation. Due to strong non-linear characteristics, existing PV model parameters identification method...

Effective and timely fault detection and status monitoring of the industrial production process is essential to fully guarantee the operational safety. However, massive multi-source heterogeneous data analysis is facing many challenges. This paper proposes a process monitoring model combined with a parallel deep learning algorithm and Principal Com...

In this paper, a deep learning fault detection and prediction framework combining principal component analysis (PCA) and Informer is proposed to solve the problem of online monitoring of nuclear power valves which is hard to implement. More specifically, PCA plays the role of dimensionality reduction and fault feature extraction. It maps data with...

Management of electric power balance requires accurate forecasting of load and generation, especially in the context of renewable energy adoption. In this context, forecasting electric load requires more attention to decrease the uncertainties in the system operation. There have been many studies under this context, however, the effect of the lookb...

The significant penetration of renewable power generations (RGs) and the large-scale use of plug-in electric vehicles (PEVs) have brought tangible impacts in tackling the climate change challenge the mankind has been facing due to substantive green-house gas and pollutant emissions from fossil-fuel based thermal power generation plants. However, th...

Unstructured texts dominate data in construction projects. With the achievements of natural language processing (NLP) techniques, mining unstructured text data for smart construction has become increasingly significant. To understand state-of-the-art NLP for smart construction, uncover related issues, and propose potential improvements , this paper...

Many cross-knowledge domain tasks involving various professional backgrounds have been transferred from construction sites to factories in modular construction (MC). In MC, forming optimal work packages which can handle the complexity of product breakdown structures and dynamic project progress is critical for task planning and execution. However,...

The teaching-learning-based optimization (TLBO) algorithm, composed of a teacher phase and a learner phase, is one of the most popular global optimization approaches. It is inevitable for TLBO to suffer from premature convergence and entrapment in local optima when dealing with complex optimization problems. To solve this problem, a novel TLBO vari...

The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real-world...