Doctor of Philosophy (Ph.D.) in Artificial Intelligence (0804J1, Academic Degree)

I. About the Program

For traditional disciplines, Artificial Intelligence (AI) serves as an integrating and enabling technology. These fields feature more concrete scientific problems with well-defined criteria, which in turn provide new opportunities and sophisticated application scenarios for the advancement of AI theories and technologies. AI researches and develops theories, methods, technologies, and application systems to simulate, extend, and expand human intelligence. It constitutes a novel technological science emerging from the convergence of computer science, engineering, and mathematics – an interdisciplinary synthesis bridging the natural and social sciences. Simultaneously, AI exhibits strong interdisciplinary convergence, penetrative capacity, and enabling capabilities. Its deep integration with traditional leading disciplines not only enhances problem-solving abilities in relevant fields and propels theoretical and technological advancements in those disciplines, but also effectively drives the evolution of AI theories and technologies while strengthening talent cultivation capabilities within the AI domain.

This program is a strategically established transdisciplinary field that integrates Shenyang University of Technology's educational heritage, strengths, and distinctive features. It addresses the practical needs of China's equipment manufacturing industry for development and innovation, drawing upon three primary disciplines—Mechanical Engineering, Electrical Engineering, and Instrument Science & Technology—while incorporating fundamental theories from Control Science and Engineering. The discipline extends its relevance to applied domains such as intelligent manufacturing and smart grids. The three foundational disciplines supporting this field—Mechanical Engineering, Electrical Engineering, and Instrument Science & Technology—are designated First-Class Key Construction Disciplines in Liaoning Province. They conduct pioneering theoretical research, cutting-edge technological development, and engineering applications in areas including equipment manufacturing, large-scale complete electrical equipment sets, and detection technology/equipment manufacturing. Collectively, these disciplines rank among China's foremost tiers in comprehensive capability. Notably, the discipline has achieved nationally leading positions in China for laser additive manufacturing, laser cleaning, and online internal inspection technologies/equipment for oil-gas pipelines, while hosting multiple provincial and municipal research platforms including the Liaoning Provincial Key Laboratory of Embedded Technology Applications (Department of Education), Shenyang Key Laboratory of Artificial Intelligence, Shenyang Laboratory for Intelligent Perception and Edge Computing, and Shenyang Key Laboratory for Innovative Applications of Industrial AI Chips and Network Systems.

In recent years, the discipline has obtained tens of national projects including National Natural Science Foundation projects and national key research and development programs and nearly a hundred provincial and municipal projects. Faculty members have published over 400 papers in high impact journals and conferences. It also has obtained 30 invention patents and published 6 textbooks.

II. Research Directions

A. Intelligent Perception and Information Processing

Addressing perception and processing challenges across automated production, pipeline inspection, and autonomous driving domains, this concentration centers on target detection/information processing, information modeling, and networked communication. Leveraging AI theory, mathematical methods, computer science, and IoT technologies, it investigates heterogeneous information processing, recognition, classification, transmission, prediction, and interpretation under diverse computing paradigms—including edge and cloud computing—to achieve practical industrial objectives in target detection, measurement, and control. Core research encompasses intelligent pipeline safety inspection, multimodal information collaborative perception, data mapping characterization with network intelligence optimization, visual cognition theory/computational methods, intelligent product defect detection, and equipment condition monitoring/fault diagnosis.

B. Smart Grid and Energy Internet

Addressing challenges in precise modeling, analysis, and control of electrical equipment/systems operating in open and harsh environments, this concentration conducts research on intelligent methodologies and technologies for operational efficiency optimization, energy consumption prediction, fault diagnosis, predictive maintenance, and optimized design of electrical apparatus. It further investigates smart dispatch and optimization of power systems incorporating renewable energy sources and energy storage devices. Research focuses particularly on holistic intelligent design of permanent magnet motors, distributed real-time information acquisition/analysis for electrical equipment, condition analysis and predictive maintenance of power assets, AI-empowered energy consumption analytics for electrical systems, and data-driven power system analysis/optimization.

C. Smart Manufacturing Systems: Theories, Technologies, and Equipment

This direction applies new-generation AI technologies to the design, manufacturing, and production control of mechanical systems, achieving integrated and deeply convergent applications of advanced manufacturing and intelligent technologies. Research prioritizes laser manufacturing equipment and smart manufacturing techniques, encompassing intelligent state recognition of workpieces/equipment with process parameter optimization, performance-coupled modeling and optimization for intelligent equipment/product design, data-driven intelligent quality control in manufacturing processes and intelligent operations & maintenance, equipment health analysis and predictive maintenance, intelligent robotics, and smart manufacturing systems engineering.

D. Intelligent Control and Optimization

Aiming to achieve industrial systems' self-perception, self-prediction, self-adaptation, and autonomous optimization capabilities in complex environments and operating regimes, this concentration addresses inherent challenges including nonlinearity, time-variance, uncertainty, unpredictability, and difficulties in establishing precise mathematical models. Leveraging AI theories and technologies, it advances research on multi-objective optimization methodologies for complex systems, fault-tolerant control approaches, and intelligent control solutions for hierarchical industrial process control systems with specialized problem-solving. Core research encompasses data-driven modeling/control/optimization of complex systems, multi-objective optimization design for fault-tolerant control systems, intelligent optimization/scheduling/decision-making, optimized control design with coordinated control of complex interconnected systems, and knowledge-based system control/optimization.

III. Program Curriculum

IV. Program Objectives

Addressing national development and scientific advancement needs, this discipline cultivates high-level innovative academic talents with well-rounded moral, intellectual, physical, aesthetic and practical capabilities for research, teaching, or technological innovation/management roles in enterprises, institutions of higher learning, and research institutes within AI and related interdisciplinary fields. Doctoral candidates pursuing academic degrees in this discipline shall meet the following requirements:

A. Doctoral candidates must demonstrate patriotism, firmly aligned political orientation, high social responsibility, rigorous and truth-seeking scientific ethos, strong professional ethics, acute academic insight, independent thinking capabilities, exemplary scholarly integrity, and sound mental/physical health.

B. They should possess comprehensive and solid foundational theories in artificial intelligence alongside systematic and profound domain expertise within their discipline, while continuously tracking cutting-edge academic developments and grasping current landscapes and evolutionary trajectories in their research fields.

C. Candidates must exhibit robust independent research capabilities to formulate valuable scientific inquiries by integrating theoretical knowledge with engineering needs, design methodical research frameworks, and produce innovative research outcomes within specialized domains of their discipline.

D. Proficiency in at least one foreign language is required, enabling fluent reading comprehension of discipline-relevant literature, effective academic writing, and demonstration of global perspective with cross-cultural communication, collaborative engagement, and competitive participation abilities.