Recent Journal Papers

The papers that are not labeled are predominantly in manufacturing and industrial automation domain.

• A. Kusiak, Predictive Models in Digital Manufacturing: Research, Applications, and Future Outlook, International Journal of Production Research, Vo. 61, No. 17, pp. 6052-6062, 2023 [Data science]. 
• A. Kusiak, Manufacturing Metaverse, Journal of Intelligent Manufacturing, Vol. 34, No. 6, 2023, pp. 2511-2512.
• G. Liu, W. Shen, L. Gao, and A. Kusiak, Active Federated Transfer Algorithm Based on Broad Learning for Fault Diagnosis, Measurement, Vol. 208, February 2023, pp. 1-10, 112452 [Data science].
• G. Liu, W. Shen, L. Gao, and A. Kusiak, Automated Broad Transfer Learning for Cross-domain Fault Diagnosis, SME Journal of Manufacturing Systems, Vol. 66, February 2023, pp. 27-41 [Data science].
• Y. Shen, Z. Song, A. Kusiak, and Z. Zhan, Prognosis of Rotor Parts Fly-off Based on Cascade Classification and Online Prediction Ability Index, Measurement Science and Technology, Vol. 34, No. 1, 2023, 12022 [Digital manufacturing].
• G. Liu, W. Shen, L. Gao, and A. Kusiak, Active Broad-Transfer Learning Algorithm for Class-imbalanced Fault Diagnosis, IEEE Transactions on Instrumentation & Measurement, Vol. 72, pp. 1-16, 2023, 3502216 [Data science]. 
• A. Sheth and A. Kusiak, Resiliency of Smart Manufacturing Enterprises via Information, Journal of Industrial Information Integration, Vol. 28, July 2022, 100370 [Smart manufacturing].
• A. Kusiak, From Digital to Universal Manufacturing, International Journal of Production Research, Vol. 60, No. 1, 2022, pp. 349-360 [Digital manufacturing]. 
• G. Liu, W. Shen, L. Gao, and A. Kusiak, An Active Label-denoising Algorithm Based on Broad Learning for Annotation of Machine Health Status, Science China: Technological Sciences, Vol. 65, No. 9, 2022, pp. 2089-2104 [Data science].
• G. Liu, W. Shen, L. Gao, and A. Kusiak, Knowledge Transfer in Fault Diagnosis of Rotary Machines, IET Collaborative Intelligent Manufacturing, Vol. 4, No. 1, 2022, pp. 17-34 [Data science].  
• A. Kusiak, Universal Manufacturing: Enablers, Properties, and Models, International Journal of Production Research, Vol. 60, No. 8, 2022, pp. 2497-2513 [Digital manufacturing]. 
• A. Dhini, I. Surjandari, B. Kusumoputro, A. Kusiak, Extreme Learning Machine–Radial Basis Function (ELM-RBF) Networks for Diagnosing Faults in a Steam Turbine, Journal of Industrial and Production Engineering, Vol. 39, No. 7, 2022, pp. 572-580 [Energy]. 
• S. Lu, Q. Xu, Y. Liu, C. Jiang, Y. Liu, and A. Kusiak, Probabilistic Load Forecasting with a Non-Crossing Sparse-Group Lasso-Quantile Regression Deep Neural Network, Energy, Vol. 242, March 1, 2022 [Energy].
• G. Liu, W. Shen, L. Gao, and A. Kusiak, Predictive Modeling with an Adaptive Unsupervised Broad Transfer Algorithm, IEEE Transactions on Instrumentation and Measurement, Vol. 70, pp. 1-12, 2021, 3520212 [Data science]. 
• T. Qin, R. Du, A. Kusiak, H. Tao, and Y. Zhong, Designing a Resilient Production System with Reconfigurable Machines and Movable Buffers, International Journal of Production Research, Vol. 60, No.17, 2022, pp. 5277-5292 [Manufacturing].  
• D. Wang, W. Tang, Y. Liu, X. Qu, Z. Ma, and A. Kusiak, An Opportunistic Maintenance Strategy for Wind Turbines, IET Renewable Power Generation, Vol. 15, No. 16, 2021, pp. 3793-3805 [Energy].
• J. Wan, X. Li, H.-N. Dai, A. Kusiak, M. Martinez-Garcia, and D. Li, Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges, Proceedings of the IEEE, Vol. 109, No. 4, 2021, pp. 377-398 [Intelligent manufacturing].
• A. Kusiak, Extreme Engineering: Polarization in Product Development and Manufacturing, Engineering, Vol. 7, No. 3, 2021, pp. 282-284 [Engineering science].
• W. Chen, Y. Qiu, Y. Feng, Y. Li, and A. Kusiak, Diagnosis of Wind Turbine Faults with Transfer Learning Algorithms, Renewable Energy, Vol. 163, 2021, pp. 2053-2067 [Wind energy].  
• A. Kusiak, Extreme Science and Engineering, Journal of Intelligent Manufacturing, Vol. 31, No. 7, 2020, pp. 1607-1610 [Science].
• A. Kusiak, Open Manufacturing: A Design-for-Resilience Approach, International Journal of Production Research, Vol. 58, No. 15, 2020, pp. 4647-4658 [Manufacturing]. 
• T. Wuest, A. Kusiak, T. Dai, and S.R. Tayur, Impact of COVID-19 on Manufacturing and Supply Networks — The Case for AI-inspired Digital Transformation, ORMS Today, Vol. 47, No. 3, 2020, pp. 1-16 [Manufacturing].
• A. Kusiak, Convolutional and Generative Adversarial Neural Networks in Manufacturing, International Journal of Production Research, Vol. 58, No. 5, 2020, pp. 1594-1604 [Manufacturing].
• Z. Tang, Y. Li, and A. Kusiak, A Deep Learning Model for Measuring Oxygen Content of Boiler Flue Gas, IEEE Access, Vol. 8, No. 1, 2020, pp. 12268-12278 [Energy].
• A. Dhini, A. Faqih, B. Kusumoputro, I. Surjandari, and A. Kusiak, Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA), International Journal of Technology, Vol. 11, No. 2, 2020, pp. 388-399 [Condition monitoring].
• A. Kusiak, Service Manufacturing: Basic Concepts and Technologies, SME Journal of Manufacturing Systems, Vol. 52, Part A, 2019, pp. 198-204 [Manufacturing]
• A. Kusiak, Fundamentals of Smart Manufacturing: A Multi-Tread Perspective, IFAC Annual Reviews in Control, Vol. 47, 2019, pp. 214-220 [Manufacturing].
• Y.H. Kuo and A. Kusiak, From Data to Big Data in Production Research: The Past and Future Trends, International Journal of Production Research, Vol. 57, Nos 15-16, 2019, pp. 4228-4853 [Manufacturing].
• J. Zhu, Y. Shen, Z. Song, D. Zhou, Z. Zhang, and A. Kusiak, Data-Driven Building Load Profiling and Energy Management, Sustainable Cities and Society, Vol. 49, 2019, pp. 1-15 [Sustainability].
• A. Kusiak, Create Jobs in Cleaning up the Environment, Nature, Vol. 558, No. 7708 (June 6), 2018, p. 30 [Sustainability].
• A. Kusiak, Smart Manufacturing, International Journal of Production Research, Vol. 56, Nos. 1–2, 2018, pp. 508-517 [Manufacturing].
• F. Tao, Q. Qi, A. Liu, and A. Kusiak, Data-Driven Smart Manufacturing, Journal of Manufacturing Systems, Vol. 48, Part C, 2018, pp. 157-169 [Manufacturing].
• Y. He and A. Kusiak, Performance Assessment of Wind Turbines: Data-Derived Quantitative Metrics, IEEE Transactions on Sustainable Energy, Vol. 9, No. 1, 2018, pp. 65-73 [Wind energy].
• A. Kusiak, Smart Manufacturing Must Embrace Big Data, Nature, Vol. 544, No. 7648 (April 6), 2017, pp. 23-25 [Manufacturing].
• H. Long, Z. Zhang, Z. Song, and A. Kusiak, Formulation and Analysis of Grid and Coordinate Models for Planning Wind Farm Layouts, IEEE Access, Vol. 5, No. 1, 2017, pp. 1810-1819 [Wind energy]. 
• T. Quyang, X.M. Zha, L. Quin, and A. Kusiak, Optimization of Time Window Size for Wind Power Ramps Prediction, IET Renewable Power Generation, Vol. 11, No. 8, 2017, pp. 1270-1277 [Wind energy].
• M.T. Sattari, A.R. Joudi, and A. Kusiak, Assessment of Different Methods for Estimation of Missing Data in Precipitation Studies, Hydrology Research,  Vol. 48, No. 4, 2017, pp. 1032-1044 [Environmental engineering].
• Y. He, A. Kusiak, T. Ouyang, and W. Teng, Data-driven Modeling of Truck Engine Exhaust Valve Failures: A Case Study, Journal of Mechanical Science and Technology, Vol. 31, No. 6, 2017, pp 2747-2757 [Condition monitoring].
• Z. Ma, W. Teng, Y. Liu, D. Wang, and A. Kusiak, Application of the Multi-Scale Enveloping Spectrogram to Detection of Weak Faults in a Wind Turbine Gearbox, IET Renewable Power Generation, Vol. 11, No. 5, 2017, pp. 578-584 [Condition monitoring].
• Z. Ma, Y. Liu, D. Wang, W. Teng, and A. Kusiak,  Cyclostationary Analysis of a Faulty Bearing in the Wind Turbine, ASME Transactions: Journal of Solar Energy Engineering, Vol. 139, No. 3,  2017, pp. 031006-1 - 031006-12 [Condition monitoring].
• W. Teng, X. Ding, Y. Zhang, Y. Liu, Z, Ma, and A. Kusiak, Application of Cyclic Coherence Function to Bearing Fault Detection in a Wind Turbine Generator under Electromagnetic Vibration, Mechanical Systems and Signal Processing, Vol. 87, Part A, March 2017, pp. 279-293 [Condition monitoring].
• T. Ouyang, A. Kusiak, and Y. He, Modeling Wind-Turbine Power Curve: A Data Partitioning and Mining Approach, Renewable Energy, Vol. 102 (Part A), 2017, pp. 1-8 [Wind energy].
• T. Ouyang, A. Kusiak, and Y. He, Predictive Model of Yaw Error in a Wind Turbine, Energy, Vol. 123, No. 1, 2017, pp. 119-130 [Wind energy].
• W. Teng, Y. Zhang, Y. Liu, A. Kusiak, and Z. Ma, Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox, Energies, Vol. 10, No. 1, 2017, pp.1-16 [Condition monitoring].
• S. Altunas, T. Dereli, and A. Kusiak, Assessment of Corporate Innovation Capability with a Data-mining Approach: Industrial Case Studies, Computers and Industrial Engineering, Vol 102, December 2016, pp. 58-68 [Innovation].
• Y. Zeng, Z. Zhang, A. Kusiak, F. Tang, and X. Wei, Optimizing Wastewater Pumping system with Data-Driven Models and a Greedy Electromagnetism-like Algorithm, Stochastic Environmental Research and Risk Assessment, Vol. 30, No. 4, 2016, pp. 1263-1275 [Wastewater process energy]. 
• M.T. Sattari, A.R. Joudi, and A. Kusiak, Estimation of Water Quality Parameters with Data-driven Models, Journal of American Water Works Association, Vol. 108, No. 4, 2016, pp. E232-E239 [Sustainability].
• A. Kusiak, Put Innovation Science at the Heart of Discovery, Nature, Vol. 530, No. 7590 (February 18), 2016, p. 255 [Innovation].
• A. Kusiak, Share Data on Wind Energy, Nature, Vol. 529, No. 7584 (January 7), 2016, pp. 19-21 [Wind energy].
• Z. Zhang, A. Kusiak, Y. Zeng, and X. Wei, Modeling and Optimization of a Wastewater Pumping System with Data-mining Methods, Applied Energy, Vol. 164, No. 2, 2016, 303-311 [Wastewater process energy]. 
• Y. Zeng, Z. Zhang, and A. Kusiak, Predictive Modeling and Optimization of a Multi-zone HVAC System with Data Mining and Firefly Algorithms, Energy, Vol. 86, No. 1, 2015, pp. 393-402 [HVAC energy].
• S. Altunas, T. Dereli, and A. Kusiak, Forecasting Technology Success Based on Patent Data, Technological Forecasting and Social Change, Vol. 96, No. 1, 2015, pp. 202-214 [Innovation].
• S. Altunas, T. Dereli, and A. Kusiak, Analysis of Patent Documents with Weighted Association Rules, Technological Forecasting and Social Change, Vol. 92, No. 1, 2015, pp. 249-262 [Innovation].
• X. Wei, A. Kusiak, M. Li, F. Tang, and Y. Zeng, Multi-objective Optimization of the HVAC (Heating, Ventilation, and Air Conditioning) System Performance, Energy, Vol. 83,  No. 1, 2015, pp. 294-306 [HVAC energy].
• Z. Zhang, X. Wei, and A. Kusiak, Data-driven Cost Minimization of Pump Operations and Maintenance, Engineering Applications of Artificial Intelligence, Vol. 40, No. 1, 2015, pp 37-46 [Wastewater process energy]. 
• X. Wei and A. Kusiak, Short-term Prediction of Influent Flow in Wastewater Treatment Plant, Stochastic Environmental Research and Risk Assessment, Vol. 29, No. 1, 2015, pp 241-249  [Wastewater process energy]. 
• X. Wei, G. Xu, A. Kusiak, Modeling and Optimization of a Chiller Plant, Energy, Vol. 73, No. 1, 2014, 898-907 [HVAC energy].
• F. Tang, A. Kusiak, X. Wei, Modeling and Short-term Prediction of HVAC System with a Clustering Algorithm, Energy and Buildings, Vol. 82, No. 1, 2014, pp. 310-321 [HVAC energy].
• A. Kusiak, G.L. Xu, and Z. Zhang, Minimization of Energy Consumption in HVAC Systems with Data-driven Models and an Interior-point Method, Energy Conversion and Management, Vol. 85, No. 1, 2014 pp. 146-153 [HVAC energy].  
• X. He, Z. Zhang, and A. Kusiak, Performance Optimization of HVAC Systems with Computational Intelligence Algorithms, Energy and Buildings, Vol. 81, No. 1, 2014, pp. 371-380 [HVAC energy].  
• A. Kusiak and X. Wei, Prediction of Methane Production in Wastewater Treatment Facility: A Data-Mining Approach, Annals of Operations Research, Vol. 216, No. 1, 2014, pp. 71-81 [Wastewater process energy]. 
• Z. Zhang, Q. Zhou, and A. Kusiak, Optimization of Wind Power and its Variability with a Computational Intelligence Approach, IEEE Transactions on Sustainable Energy, Vol. 5, No. 1, 2014, pp.  228-236  [Wind energy].
• A. Kusiak, Z. Zhang, and A. Verma, Prediction, Operations, and Condition Monitoring in Wind Energy, Energy, Vol. 60, No. 1, 2013, pp. 1-12 [Wind energy].
• A. Kusiak, Z. Zhang, and G. Xu, Minimization of Wind Farm Operational Cost Based on Data-driven  Models, IEEE Transactions on Sustainable Energy, Vol. 4, No. 3, 2013, pp. 756-764 [Wind energy].
• M.V. Muste, D.A. Bennett, S.Secchi, J.L. Schnoor, A. Kusiak, N.J. Arnold, S.K. Mishra, D. Ding, and U. Rapolu, End-to-End Cyberinfrastructure for Decision-Making Support in Watershed Management, ASCE Journal of Water Resources Planning and Management, Vol. 139, No. 5, 2013, pp. 565-573 [Water quality]. 
• A. Kusiak, Y. Zeng, Z. Zhang, Modeling and Analysis of Pumps in a Wastewater Treatment Plant: A Data-Mining Approach, Engineering Applications of Artificial Intelligence, Vol. 26, No. 7, 2013, pp. 1643-1651 [Wastewater process energy]. 
• A. Kusiak, X. Wei, A. Verma, and E. Roz, Modeling and Prediction of Rainfall Using Radar Reflectivity Data: A Data-Mining Approach, IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 4, 2013, pp. 2337-2342 [Water quality].
• A. Verma, Z. Zhang, and A. Kusiak, Modeling and Prediction of Gearbox Faults with Data-Mining Algorithms, ASME Transactions: Journal of Solar Energy Engineering, Vol. 135, No. 3,  2013, pp. 031007-1 - 031007-11 [Wind energy].
• A. Verma, X. Wei, and A. Kusiak, Predicting the Total Suspended Solids in Wastewater: A Data-Mining Approach, Engineering Applications of Artificial Intelligence, Vol. 26, No. 4, 2013, pp. 1366-1372 [Wastewater process energy]. 
• A. Kusiak and X. Wei, Optimization of the Activated Sludge Process, ASCE Journal of Energy Engineering, Vol. 139, No. 1, 2013, pp. 12-17 [Wastewater process energy]. 
• A. Kusiak, Y. Zeng, and G. Xu, Minimizing Energy Consumption of an Air Handling Unit with a Computational Intelligence Approach, Energy and Buildings, Vol. 60, No. 1, 2013, pp. 355-363 [HVAC energy].
• A. Kusiak and A. Verma, Monitoring Wind Farms with Performance Curves, IEEE Transactions on Energy Conversion, Vol. 4, No. 1, 2013, pp. 192-199 [Wind energy]. 
• A. Kusiak, A. Verma, and X. Wei, A Data-Mining Approach to Predict Influent Quality, Environmental Monitoring and Assessment, Vol. 185, No. 3, 2013, pp. 2197-2210 [Wastewater process energy].
• Y. Jiang, Z. Song, and A. Kusiak, Very Short-term Wind Speed Forecasting with Bayesian Structural Break Model, Renewable Energy, Vol. 50, 2013, pp. 637-647 [Wind energy].
•  Z. Zhang, A. Kusiak, and Z. Song, Scheduling Electric Power Production at a Wind Farm, European Journal of Operational Research, Vol. 224, No. 1, 2013, pp. 227-238 [Wind energy]. 
• Z. Zhang, Y. Zeng, and A. Kusiak, Minimizing Pump Energy in a Wastewater Processing Plant, Energy, Vol. 47,  No. 1, 2012, pp. 505-514 [Wastewater process energy].     
• A. Kusiak and A. Verma, Analyzing Bearing Faults in Wind Turbines: A Data-Mining Approach, Renewable Energy, Vol. 48, No. 1, 2012, pp.110-116 [Wind energy].
• Z. Zhang, and A. Kusiak, Monitoring Wind Turbine Vibration Based on SCADA Data, ASMETransactions: Journal of Solar Energy Engineering, Vol. 134, No. 2, 2012, pp. 021004-1-021004-12.
• Z. Zhang, A. Verma, and A. Kusiak, Fault Analysis of the Wind Turbine Gearbox, IEEE Transactions on Energy Conversion, Vol. 27, No. 2, 2012, pp. 526-535 [Wind energy]. 
• A. Kusiak and G.L. Xu, Modeling and Optimization of HVAC Systems Using a Dynamic Neural Network, Energy, Vol. 42, No. 1,  2012, pp. 241-250 [HVAC energy].
• A. Verma and A. Kusiak, Fault Monitoring Wind Turbine Generator Brushes: A Data Mining Approach, ASME Transactions: Journal of Solar Energy Engineering, Vol. 134, No. 2, 2012, pp. 021001-1-021001-9 [Wind energy]. 
• A. Kusiak and X. Wei, A Data-Driven Model for Maximization of Methane Production in a Wastewater Treatment Plant, Water Science and Technology, Vol. 65, No. 6, 2012, pp. 1116-1122 [Wastewater process energy].  
• A. Kusiak and Z. Zhang, Control of Wind Turbine Power and Vibration with a Data-Driven Approach, Renewable Energy, Vol. 43, Vol. 7, 2012, pp. 73-82 [Wind energy]. 
• A. Kusiak and A. Verma, A Data-Mining Approach to Monitoring Wind Turbines, IEEE Transactions on Sustainable Energy, Vol. 3, No.1, 2012, pp. 150-157 [Wind energy]. 
• Z. Zhang and A. Kusiak, Optimization of Energy Consumption of Pumps in a Wastewater Processing Plant, ASCE Journal of Energy Engineering, Vol. 137, No. 4, 2011, pp. 159-168 [Wastewater process energy].
• A. Kusiak, G.L. Xu, and F. Tang, Optimization of an HVAC System with a Strength Multi-objective Particle-Swarm Algorithm, Energy, Vol. 36, No. 10, 2011, pp. 5935-5943 [HVAC energy].
• A. Kusiak, H.Y. Zheng, and Z. Zhang, A Wind Speed Virtual Sensor for Wind Turbines, ASCE Journal of Energy Engineering, Vol. 137, No. 2, 2011, pp. 60-69 [Wind energy]. 
• J. Engler and A. Kusiak, Modeling an Innovation Ecosystem with Adaptive Agents, International Journal of Innovation Science, Vol. 3, No. 2, 2011, pp. 55-68 [Innovation science].
• A. Kusiak, F. Tang, and G.L. Xu, Multi-Objective Optimization of HVAC System with an Evolutionary Computation Algorithm, Energy, Vol. 36, No. 5, 2011, pp. 2440-2449 [HVAC energy].
• Z. Song, X. Geng, A. Kusiak, and C. Xu, Mining Markov Chain Transition Matrix from Wind Speed Time Series Data, Expert Systems with Applications, Vol. 38, No. 8, 2011, pp. 10229-10239 [Wind energy]. 
• A. Kusiak and A. Verma, Prediction of Status Patterns of Wind Turbines: A Data-Mining Approach, ASMETransactions: Journal of Solar Energy Engineering, Vol. 133, No. 1,  2011, pp. 011008-1 - 011008-10 [Wind energy]. 
• A. Kusiak and A. Verma, A Data-driven Approach for Monitoring Blade Pitch Faults in Wind Turbines, IEEE Transactions on Sustainable Energy, Vol. 2, No.1, 2011, pp. 87-96 [Wind energy].
• A. Kusiak and Z. Zhang, Adaptive Control of a Wind Turbine with Data Mining and Swarm Intelligence, IEEE Transactions on Sustainable Energy, Vol. 2, No. 1, 2011, pp. 28-36 [Wind energy].
•  A. Kusiak and W.Y. Li, The Prediction and Diagnosis of Wind Turbine Faults, Renewable Energy, Vol. 36, No. 1, 2011, pp. 16-23 [Wind energy].
•  A. Kusiak and Z. Zhang, Short-Horizon Prediction of Wind Power: A Data-Driven Approach, IEEE Transactions on Energy Conversion, Vol. 25, No. 4, 2010, pp. 1112-1122 [Wind energy].
• A. Kusiak, Z. Zhang, and M.Y. Li, Optimization of Wind Turbine Performance with Data-Driven Models, IEEE Transactions on Sustainable Energy, Vol. 1, No. 2, 2010, pp. 66-76 [Wind energy].
•  A. Kusiak, M.Y. Li, F. Tang, Modeling and Optimization of HVAC Energy Consumption, Applied Energy, Vol. 87, No. 10, 2010, pp. 3092-3102 [HVAC energy].
•  A. Kusiak and W.Y. Li, Estimation of Wind Speed with Wind Turbine Affinity: A Data-Driven Approach, Journal of Wind Engineering & Industrial Aerodynamics, Vol. 98, No. 10-11, 2010, pp. 559-567 [Wind energy].
• A. Kusiak and Z. Zhang, Analysis of Wind Turbine Vibrations Based on SCADA Data, ASME Transactions: Journal of Solar Energy Engineering, Vol. 132, No. 3, 2010, pp. 031008-1 - 031008-12 [Wind energy].
• A. Kusiak and W.Y. Li, Short-Term Prediction of Wind Power with a Clustering Approach, Renewable Energy, Vol. 30, No. 10, 2010, pp. 2362-2369 [Wind energy].
• A. Kusiak, M.Y. Li, and H.-Y. Zheng, Virtual Models of Indoor-Air-Quality Sensors, Applied Energy, Vol. 87, No. 6, 2010, pp. 2087-2094 [HVAC energy].
• A. Kusiak and M.Y. Li, Reheat Optimization of the Variable-Air-Volume Box, Energy, Vol. 35, No. 5, 2010, pp. 1997-2005 [HVAC energy].
• A. Kusiak and W. Li, Virtual Models for Prediction of Wind Turbine Parameters, IEEE Transactions on Energy Conversion, Vol. 25, No. 1, 2010, pp. 245-252 [Wind energy].
• A. Kusiak and H.-Y. Zheng, Optimization of Wind Turbine Energy and Power Factor with an Evolutionary Computation Algorithm, Energy, Vol. 35, No. 3, 2010, pp. 1324–1332 [Wind energy].
• Z. Song and A. Kusiak, Multi-Objective Optimization of Temporal Processes, IEEE Transactions on Systems, Man, and Cybernetics: Part B, Vol. 40, No. 3, 2010, pp. 845-856 [Wind energy].
• A. Kusiak, M.Y. Li, and Z. Zhang, A Data-driven Approach for Steam-Load Prediction in Buildings, Applied Energy, Vol. 87, No. 3, 2010, pp. 925-933 [HVAC energy].
• A. Kusiak, Z. Song, and H.-Y. Zheng, Power Optimization of Wind Turbines with Data Mining and Evolutionary Computation, Renewable Energy, Vol. 35, No. 3, 2010, pp. 695-702 [Wind energy].
•  A. Kusiak and M.Y. Li, Cooling Output Optimization of an Air Handling Unit, Applied Energy, Vol. 87, 2010, No. 3, pp. 901-909 [HVAC energy].
• A. Kusiak and Zeng. Song, Design of Wind Farm Layout for Maximum Wind Energy Capture, Renewable Energy, Vol. 35, No. 3, 2010, pp. 685-694 [Wind energy].
• J. Engler and A. Kusiak, Mining Authoritativeness of Collaborative Innovation Partners, International Journal of Computers, Communications & Control, Vol. 5, No. 1, 2010, pp. 45-54 [Innovation science].
• C. Da-Cunha, B. Agard, and A. Kusiak, Selection of Modules for Mass Customization, International Journal of Production Research, Vol. 48, No. 5, 2010, pp. 1439-1454 [Mass customization].
• S. Shah and A. Kusiak, Relabeling Algorithm for Retrieval of Noisy Instances and Improving Prediction Quality, Computers in Biology and  Medicine, Vol. 4, No. 3, 2010, pp. 288-299  [Medical topic].
• A. Kusiak and Z. Song, Sensor Fault Detection in Power Plants, ASCE Journal of Energy Engineering, Vol. 135, No. 4, 2009,  pp. 127-137 [Energy].
• A. Kusiak, W. Li, and Z. Song, Dynamic Control of Wind Turbines, Renewable Energy, Vol. 35, No. 2, 2010, pp. 456–463 [Wind energy].
• A. Kusiak and M.Y. Li, Optimal Decision Making in Ventilation Control, Energy, Vol. 34, No. 11,  2009, pp. 1835-1845 [HVAC energy].
• Z. Song and A. Kusiak, Mining Pareto-Optimal Modules for Delayed Product Differentiation, European Journal of Operational Research, Vol. 201, No. 1,  2010, pp. 123–128 [Manufacturing].
• A. Kusiak, Z. Song, and H.-Y. Zheng, Anticipatory Control of Wind Turbines with Data-Driven Predictive Models, IEEE Transactions on Energy Conversion, Vol. 24, No. 3, 2009, pp. 766-774 [Energy].
• H.-Y. Zheng and A. Kusiak, Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach, ASME Journal of Solar Engineering, Vol. 131, No. 3, 2009, pp. 031011-1-031011-8 [Wind energy]. 
• A. Kusiak, H.-Y. Zheng, and Z. Song, Wind Farm Power Prediction: A Data-Mining Approach, Wind Energy, Vol. 12, No. 3, 2009, pp. 275-293 [Wind energy].
• A. Kusiak, H.-Y. Zheng, and Z. Song, Short-Term Prediction of Wind Farm Power: A Data-Mining Approach, IEEE Transactions on Energy Conversion, Vol. 24, No. 1, 2009, pp. 125-136 [Wind energy].
• A. Kusiak, H.-Y. Zheng, and Z. Song, On-line Monitoring of Power Curves, Renewable Energy, Vol. 34, No. 6, 2009, pp. 1487-1493 [Wind energy].
• Z. Song and A. Kusiak, Optimizing Product Configurations with a Data-Mining Approach, International Journal of Production Research, Vol. 47, No 7, 2009, pp. 1733-1751 [Manufacturing].
• A. Kusiak, Innovation: A Data-Driven Approach, International Journal of Production Economics, Vol. 122, No. 1, 2009, pp. 440-448 [Innovation acience].
• Z. Song and A. Kusiak, Optimization of Temporal Processes: A Model-Predictive Control Approach, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 1, 2009, pp. 169-179  [Energy].
• A. Kusiak, H.-Y. Zheng, and Z. Song, Models for Monitoring Wind Farm Power, Renewable Energy, Vol. 34, No. 3, 2009, pp. 583-590  [Wind energy].
• A. Kusiak and Z. Song, Clustering-Based Performance Optimization of the Boiler-Turbine System, IEEE Transactions on Energy Conversion, Vol. 23,  No. 2,  2008, pp. 651-658 [Energy topic].
• A. Kusiak and A. Salustri, Computational Intelligence in Product Design Engineering: Review and Trends, IEEE Transactions on Systems, Man, and Cybernetics: Part C, Vol. 37, No. 5, 2007, pp. 766 -778  [Engineering design].
• C. da Cunha, B. Agard, and A. Kusiak, Design for Cost: Module-Based Mass Customization, IEEE Transactions in Automation Science and Engineering, Vol. 4, No. 3, 2007, pp. 350-359 [Mass customization].
• A. Kusiak, M.R. Smith, and Z. Song, Planning Product Configurations Based on Sales Data, IEEE Transactions on Systems, Man, and Cybernetics: Part C, Vol. 37, No. 4, 2007, pp. 602-609 [Mass customization].
• A. Kusiak, Innovation: The Living Laboratory Perspective, Computer-Aided Design and Applications, Vol. 4, No. 6, 2007, pp. 863-876  [Innovation science].
• A. Kusiak and M. Smith, Data Mining in Design of Products and Production Systems, IFAC Annual Reviews in Control, Vol. 31, No. 1, 2007, pp. 147-156 [Design and manufacturing].
• A. Kusiak, Innovation Science: A Primer, International Journal of Computer Applications in Technology, Vol. 28, No. 2-3, 2007, pp. 140-149 [Innovation science].
• Z. Song and A. Kusiak, Constraint-Based Control of Boiler Efficiency:  A Data-Mining Approach, IEEE Transactions on Industrial Informatics, Vol. 3, No. 1, 2007, pp. 73-83 [Energy].
• S. Shah and A. Kusiak, Cancer Gene Search with Data-Mining and Genetic Algorithms, Computers in Biology and Medicine,  Vol. 37, No. 2, 2007, pp. 251-261 [Genetics].
• A. Kusiak, Data Mining: Manufacturing and Service Applications, International Journal of Production Research, Vol. 44, No. 18-19, 2006, pp. 4175-4191 [Manufacturing].
• J.A. Harding, M. Shahbaz, S. Srinivas, and A. Kusiak, Data Mining in Manufacturing: A Review, ASME Transactions: Journal of Manufacturing Science and Engineering, Vol. 128, No. 4, 2006, pp. 969-976 [Manufacturing].
• C. Da Cunha, B. Agard, and A. Kusiak, Data Mining for Improvement of Product Quality, International Journal of Production Research, Vol. 44, No. 18-19, 2006, pp. 4027-4041 [Quality].
• A. Kusiak and Z. Song, Combustion Efficiency Optimization and Virtual Testing: A Data-Mining Approach, IEEE Transactions on Industrial Informatics, Vol. 2, No. 3, 2006, pp. 176-184 [Energy topic].
• A. Kusiak and S. Shah, A Data-Mining-Based System for Prediction of Water Chemistry Faults, IEEE Transactions on Industrial Electronics, Vol. 53, No. 2, 2006, pp. 593-603 [Energy].
• A. Kusiak, C.A. Caldarone, M.D. Kelleher, F.S. Lamb, T.J. Persoon, and A. Burns, Hypoplastic Left Heart Syndrome: Knowledge Discovery with a Data Mining Approach, Computers in Biology and Medicine, Vol. 36, No. 1, 2006, pp.  21-40 [Medical topic].
• J. Feng, Z.-G. Yu, and A. Kusiak, Selection and Validation of Predictive Regression and Neural Networks Models Based on Designed Experiments, IIE Transactions: Design and Manufacturing, Vol. 38, No. 1, 2006, pp. 13-23 [Quality]. 
• S. Shah, A. Kusiak, and M. O’Donnell, Patient-Recognition Data-Mining Model for BCG-plus Interferon Immunotherapy Bladder Cancer Treatment, Computers in Biology and Medicine, Vol. 36, No. 6, 2006, 634-655 [Medical topic].
• A. Kusiak, Data Farming: A Primer, International Journal of Operations Research, Vol. 2, No. 2, 2005, pp. 48-57 [Data farming].
• A. Kusiak, A. Burns, and F. Milster, Optimizing Combustion Efficiency of a Circulating Fluidized Boiler: A Data Mining Approach, International Journal of Knowledge-Based and Intelligent Engineering Systems, Vol. 9, No. 4, 2005, pp. 263-274 [Energy topic].
• A. Kusiak, A. Burns, S. Shah, and N. Novotny, Detection of Events Causing Pluggage of a Coal-Fired Boiler: A Data Mining Approach, Combustion Science and Technology, Vol. 177, No. 12, 2005, pp. 2327-2348 [Energy topic].
• C.-C. Huang, T.-L. Tseng, and A. Kusiak, XML-Based Modeling of Corporate Memory, IEEE Transactions on Systems, Man, and Cybernetics: Part A, Vol. 35,  No. 5,  2005, pp. 629-640 [Knowledge management].
• K. Park and A. Kusiak, Enterprise Resource Planning (ERP) Operations Support System for Maintaining Process Integration, International Journal of Production Research, Vol. 49, No. 43, 2005, pp. 3959-3982 [Manufacturing].
• A. Kusiak, B. Dixon, and S. Shah, Predicting Survival Time for Kidney Dialysis Patients: A Data Mining Approach, Computers in Biology and Medicine, Vol. 35, No. 4, 2005, pp. 311-327 [Medical topic].
• B. Agard and A. Kusiak, Data Mining for Subassembly Selection, ASME Transactions: Journal of Manufacturing Science and Engineering, Vol. 126, No. 3, 2004, pp. 627-631. 
• S.Shah and A. Kusiak, Data Mining and Genetic Programming Based Gene/SNP Selection, Artificial Intelligence in Medicine, 2004, Vol. 31, No. 3, pp. 183-196 [Genetics].
• B. Agard and A. Kusiak, A Data-Mining Based Methodology for the Design of Product Families, International Journal of Production Research, 2004, Vol. 42, No. 15, pp. 2955-2969.
• A. Kusiak, A Data Mining Approach for Generation of Control Signatures, ASME Transactions: Journal of Manufacturing Science and Engineering, Vol. 124, No. 4, 2002, pp. 923-926.
• A. Kusiak, Integrated Product and Process Design: A Modularity Perspective, Journal of Engineering Design, Vol. 13, No. 3, 2002, pp. 223-231. 
• A. Kusiak, Feature Transformation Methods in Data Mining, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 24, No. 3, 2001, pp. 214 -221.
• A. Kusiak, I.H. Law, M.D. Dick, The G-Algorithm for Extraction of Robust Decision Rules: Children’s Postoperative Intra-atrial Arrhythmia Case Study, IEEE Transactions on Information Technology in Biomedicine, Vol. 5, No. 3, 2001, pp. 225-235 [Medical topic].
• A. Kusiak and C. Kurasek, Data Mining of Printed-Circuit Board Defects, IEEE Transactions on Robotics and Automation, Vol. 17, No. 2, 2001, pp. 191-196
• A. Kusiak, Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 24, No. 1, 2001, pp. 44-50.
• A. Zakarian and A. Kusiak, Process Analysis and Reengineering, Computers and Industrial Engineering, Vol. 41, No. 2, 2001, pp. 135-150.
• D. He, A. Babayan, and A. Kusiak, Scheduling Manufacturing Systems in an Agile Environment, Robotics and Computer Integrated Manufacturing, Vol. 17, No. 1-2, 2001, pp. 87-97.
• A. Kusiak, Decomposition in Data Mining: An Industrial Case Study, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 4, 2000, pp. 345-353.
• A. Kusiak, J.A. Kern, K.H. Kernstine, and T.L. Tseng, Autonomous Decision-Making: A Data Mining Approach, IEEE Transactions on Information Technology in Biomedicine, Vol. 4, No. 4, 2000, pp. 274-284 [Medical topic].
• A. Kusiak, Evolutionarynary Computation in Process Modeling and Programming, Robotica, Vol. 40, No. 3, 2000, pp. 15-19.
• A. Zakarian and A. Kusiak, Analysis of Process Models, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 2, 2000, pp. 137- 147.
• A. Kusiak, Evolutionary Computation in Generation of Process Models, International Journal of Agile Manufacturing, Vol. 3, No. 2, 2000, pp. 53-70.
• L. Al-Hakim, A. Kusiak, and J. Mathew, A Graph-theoretic Approach to Conceptual Design with Functional Perspectives, Computer-Aided Design, Vol. 32, No. 14, 2000, pp. 867-875.
• C.X. Feng and A. Kusiak, Robust Tolerance Synthesis with the Design of Experiments Approach, ASME Transactions: Journal of Manufacturing Science and Engineering, Vol. 122, No. 3, 2000, pp. 520-528.
• D.W. He, B. Strege, H. Tolle, and A. Kusiak, Decomposition in Automatic Generation of Petri Nets for Manufacturing System Control and Scheduling, International Journal of Production Research, Vol. 38, No. 6, 2000,  pp. 1437-1457.
• C.C. Huang and A. Kusiak, Synthesis of Modular Mechatronic Products: A Testability Perspective, IEEE/ASME Transactions on Mechatronics, Vol. 4, No. 2, 1999, pp. 119-132.
• A. Zakarian and A. Kusiak, Forming Teams: An Analytical Approach, IIE Transactions on Design and Manufacturing, Vol. 31, No. 1, 1999, pp. 85-97.
• C.C. Huang and A. Kusiak, Modularity in Design of Products and Systems, IEEE Transactions on Systems, Man, and Cybernetics: Part A, Vol. 28, No.1, 1998, 66-77.
• A. Kusiak and D. He, Design for Agility: A Scheduling Perspective, Robotics and Computer-Integrated Manufacturing Systems, Vol. 14, No. 4, 1998, pp. 415-427.
• D. He, A. Kusiak, and T. L. Tseng, Delayed Product Differentiation: A Design and Manufacturing Perspective, Computer-Aided Design, Vol. 30, No. 2, 1998, pp. 105-113.
• D. He and A. Kusiak, Designing an Assembly System for Modular Products, Computers and Industrial Engineering, Vol. 31, No. 1, 1998, pp. 37-52.
• C.C. Huang and A. Kusiak, Manufacturing Control withh a Push-pull Control, International Journal of Production Research, Vol. 36, No. 1, 1998, pp. 251-275.
• D. He and A. Kusiak, Design of Assembly Systems for Modular Products, IEEE Transactions on Robotics and Automation, Vol. 13, No. 5, 1997, pp. 646-655.
• C.X. Feng and A. Kusiak, Design of Tolerances with the Extended Quality Loss Function, ASME Transactions: Journal of Manufacturing Science and Engineering, Vol. 119, No. 4 (A), 1997, pp. 603-610.
• C.X. Feng, A. Kusiak, and C.C. Huang, A Scheduling Model for Setup Time Reduction, ASME Transactions: Journal of Manufacturing Science and Engineering, Vol. 119, No. 4 (A), 1997, pp. 571-579.
• A. Kusiak, T. Letsche, and A. Zakarian, Data Modeling with IDEF1x, International Journal of Computer Integrated Manufacturing, Vol. 10, No. 6, 1997, pp. 470-486.
• A. Kusiak and C.C. Huang, Design of Modular Digital Circuits for Testability, IEEE Transactions on Components, Packaging, and Manufacturing Technology, Part C, Vol. 20, No. 1, 1997, pp. 48-57.
• T. N. Larson, H. March, and A. Kusiak, A Heuristic Approach to Warehouse Layout with Class-Based Storage, IIE Transactions: Design and Manufacturing, Vol. 29, No. 4, 1997, pp. 337-348.
• A. Zakarian and A. Kusiak, Modeling Manufacturing Dependability, IEEE Transactions on Robotics and Automation, Vol. 13, No. 2, 1997, pp. 161-168.
• A. Kusiak and G.H. Lee, Design of Parts and Manufacturing Systems for Reliability and Maintainability, International Journal of Advanced Manufacturing Technology, Vol. 13, No. 1, 1997, pp. 67-76.
• U. Belhe and A. Kusiak, Dynamic Scheduling of Design Activities with Resource Constraints, IEEE Transactions on Systems, Man, and Cybernetics: Part A, Vol. 27, No. 1, 1997, pp. 105-111.
• A. Kusiak and D. He, Design for Agile Assembly: An Operationsl Perspective, International Journal of Production Research, Vol. 35, No. 1, 1997, pp. 157-178.
• Books
• Research in Progress