A. Journal Papers

Smart Energy

1.        W. Hua, H. Xiao, W. Pei, W.-Y. Chiu, J. Jiang, H. Sun, and P. Matthews, “Transactive energy and flexibility provision in multi-microgrids using Stackelberg game,” CSEE J. Power Energy Syst., vol. 9, no. 2, pp. 505–515, Mar. 2023.

2.        B.-C. Lai, W.-Y Chiu, and Y.-P. Tsai, “Multiagent reinforcement learning for community energy management to mitigate peak rebounds under renewable energy uncertainty,” IEEE Trans. Emerg. Topics Comput. Intell., vol. 6, no. 3, pp. 568–579, Jun. 2022.

3.        Y.-C. Chuang and W.-Y. Chiu, “Deep reinforcement learning based pricing strategy of aggregators considering renewable energy,” IEEE Trans. Emerg. Topics Comput. Intell., vol. 6, no. 3, pp. 499–508, Jun. 2022.

4.        W.-Y. Chiu, W.-K. Hsieh, C.-M. Chen, and Y.-C. Chuang, “Multiobjective demand response for Internet data centers,” IEEE Trans. Emerg. Topics Comput. Intell., vol. 6, no. 2, pp. 365–376, Apr. 2022.

5.        W.-Y. Chiu, C.-W. Hu, and K.-Y. Chiu, “Renewable energy bidding strategies using multiagent Q-learning in double-sided auctions,” IEEE Syst. J., vol. 16, no. 1, pp. 985–996, Mar. 2022.

6.        S.-J. Chen, W.-Y. Chiu, and W.-J. Liu, “User preference-based demand response for smart home energy management using multiobjective reinforcement learning,” IEEE Access, vol. 9, pp. 161627–161637, Dec. 2021.

7.        W.-Y. Chiu, J.-T. Hsieh, and C.-M. Chen, “Pareto optimal demand response based on energy costs and load factor in smart grid,” IEEE Trans. Ind. Informat., vol. 16, no. 3, pp. 1811–1822, Mar. 2020.

8.        H.-H. Chang, W.-Y. Chiu, H. Sun, and C.-M. Chen, “User-centric multiobjective approach to privacy preservation and energy cost minimization in smart home,” IEEE Syst. J., vol. 13, no. 1, pp. 1030–1041, Mar. 2019.

9.        D. Li, W.-Y. Chiu, H. Sun, and H. V. Poor, “Multiobjective optimization for demand side management program in smart grid,” IEEE Trans. Ind. Informat., vol. 14, no. 4, pp. 1482–1490, Apr. 2018.

10.    W.-Y. Chiu, H. Sun, and H. V. Poor, “A multiobjective approach to multimicrogrid system design,” IEEE Trans. Smart Grid, vol. 6, no. 5, pp. 2263–2272, Sep. 2015.

11.    W.-Y. Chiu, H. Sun, and H. V. Poor, “Energy imbalance management using a robust pricing scheme,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 896–904, Jun. 2013.

 

System and Control

12.    C.-F. Wu, C.-K. Hwang, and W.-Y. Chiu, “A Grid-based searching algorithm for solving observer-based multiobjective control design problem of nonlinear stochastic jump-diffusion system,” IET Control Theory Appl., pp. 1–16, Mar. 2023.

13.    A. Singh, W.-Y. Chiu, S. H. Manoharan, and A. M. Romanov, “Energy-efficient gait optimization of snake-like modular robots by using multiobjective reinforcement learning and a fuzzy inference system,” IEEE Access, vol. 10, pp. 86624–86635, Aug. 2022.

14.    A. M. Romanov, V. D. Yashunskiy, and W.-Y. Chiu, “A modular reconfigurable robot for future autonomous extraterrestrial missions,” IEEE Access, vol. 9, pp. 147809–147827, Nov. 2021.

15.    A. M. Romanov, M. P. Romanov, S. V. Manko, M. A. Volkova, W.-Y. Chiu, H.-P. Ma, and K.-Y. Chiu, “Modular reconfigurable robot distributed computing system for tracking multiple objects,” IEEE Syst. J., vol. 15, no. 1, pp. 802–813, Mar. 2021.

16.    W.-Y. Chiu, “Method of reduction of variables for bilinear matrix inequality problems in system and control designs,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 47, no. 7, pp. 1241–1256, Jul. 2017.

17.    W.-Y. Chiu, “Multiobjective controller design by solving a multiobjective matrix inequality problem,” IET Control Theory Appl., vol. 8, no. 16, pp. 1656–1665, Nov. 2014.

 

Signal and System

18.    Y.-C. Chuang, W.-Y. Chiu, R. Y. Chang, and Y.-C. Lai, “Deep reinforcement learning for energy efficiency maximization in cache-enabled cell-free massive MIMO networks: Single- and multi-agent approaches,” IEEE Trans. Veh. Technol., accepted.

19.    C.-L. Chuang, W.-Y. Chiu, and Y.-C. Chuang, “Dynamic multiobjective approach for power and spectrum allocation in cognitive radio networks,” IEEE Syst. J., vol. 15, no. 4, pp. 5417–5428, Dec. 2021.

20.    W.-Y. Chiu, S. H. Manoharan, and T.-Y. Huang, “Weight induced norm approach to group decision making for multiobjective optimization problems in systems engineering,” IEEE Syst. J., vol. 14, no. 2, pp. 1580–1591, Jun. 2020.

21.    W.-Y. Chiu, G. G. Yen, and T.-K. Juan, “Minimum Manhattan distance approach to multiple criteria decision making in multiobjective optimization problems,” IEEE Trans. Evol. Comput., vol. 20, no. 6, pp. 972–985, Dec. 2016.

22.    W.-Y. Chiu, B.-S. Chen, and H. V. Poor, “A multiobjective approach for source estimation in fuzzy networked systems,” IEEE Trans. Circuits Syst. I., vol. 60, no. 7, pp. 1890–1900, Jul. 2013.

23.    H. Sun, W.-Y. Chiu, and A. Nallanathan, “Adaptive compressive spectrum sensing for wideband cognitive radios,” IEEE Commun. Lett., vol. 16, no. 11, pp. 1812–1815, Nov. 2012.

24.    H. Sun, W.-Y. Chiu, J. Jiang, A. Nallanathan, and H. V. Poor, “Wideband spectrum sensing with sub-Nyquist sampling in cognitive radios,” IEEE Signal Process. Lett., vol. 60, no. 11, pp. 6068–6073, Nov. 2012.

25.    W.-Y. Chiu and B.-S. Chen, and C.-Y. Yang, “Robust relative location estimation in wireless sensor networks with inexact position problem,” IEEE Trans. Mobile Comput., vol. 11, no. 6, pp. 935–946, Jun. 2012.  

26.    W.-Y. Chiu and B.-S. Chen, “Multisource prediction under nonlinear dynamics in WSNs using a robust fuzzy approach,” IEEE Trans. Circuits Syst. I., vol. 58, no. 1, pp. 137–149, Jan. 2011. 

27.    W.-Y. Chiu and B.-S. Chen, “A mixed-norm approach using simulated annealing with changeable neighborhood for mobile location estimation,” IEEE Trans. Mobile Comput., vol. 9, no. 5, pp. 633–642, May 2010.

28.    W.-Y. Chiu and B.-S. Chen, “Mobile positioning problem in Manhattan-like urban areas: Uniqueness of solution, optimal deployment of BSs, and fuzzy implementation,” IEEE Trans. Signal Process., vol. 57, no. 12, pp. 4918–4929, Dec. 2009.

29.    W.-Y. Chiu and B.-S. Chen, “Mobile location estimation in urban areas using mixed Manhattan/Euclidean norm and convex optimization,” IEEE Trans. Wireless Commun., vol. 8, no. 1, pp. 414–423, Jan. 2009.

 

B. Book Chapters

1.        D. Li, W.-Y. Chiu, and H. Sun, “Demand side management in microgrid control systems,” in Microgrid: Advanced Control Methods and Renewable Energy System Integration, M. S. Mahmoud, Ed. Elsevier Science & Technology, Oct. 2016, pp. 203–230. 

2.        W.-K. Hsieh and W.-Y. Chiu, “Multiobjective optimization for smart grid system design,” in Smarter Energy: From Smart Metering to the Smart Grid, H. Sun, N. Hatziargyriou, L. Carpanini, M. Sánchez, and H. V. Poor, Eds. IET, Oct. 2016, pp. 193–207.

 

C. Conference Papers

Smart Energy

1.        M. Correa-Delval, H. Sun, P. Matthews, and W.-Y. Chiu, “Appliance scheduling optimisation method using historical data in households with RES generation and battery storage systems,” in Proc. Int. Conf. Renewable Energy and Power Engineering, Beijing, China, Sep. 2022, pp. 442–447.

2.        Y.-H. Lin and W.-Y Chiu, “Reinforcement learning based electricity price controller in smart grids,” in Proc. Int. Conf. Control, Automation Syst., Jeju, Korea, Oct. 2021, pp. 1820–1824.

3.        W.-Y. Chiu and G.-T. Lin, “A cross-layer design for power flow control in smart grids,” in Proc. Asian Control Conf., Gold Coast, Australia, Dec. 2017, pp. 1584–1589. 

4.        D. Li, W. Hua, H. Sun, and W.-Y. Chiu, “Multiobjective optimization for carbon market scheduling based on behavior learning,” in Proc. Int. Conf. Appl. Energy, Cardiff, United Kingdom, Aug. 2017, pp. 2089–2094.

5.        D. Li, H. Sun, and W.-Y. Chiu, “Achieving low carbon emission using smart grid technologies,” in Proc. IEEE Veh. Technol. Conf. Workshops, Sydney, Australia, Jun. 2017, pp. 1–5.

6.        W.-Y. Chiu, “Analysis of an H∞ design for dynamic pricing in the smart grid,” in Proc. IEEE Conf. Decision Control, Las Vegas, NV, USA, Dec. 2016, pp. 3234–3239. (top-tier conference in control engineering)

7.        D. Li, H. Sun, and W.-Y. Chiu, “A layered approach for enabling demand side management in smart grid,” in Proc. Int. Conf. Control, Automation Inform. Sci., Ansan, South Korea, Oct. 2016, pp. 54–59.

8.        H.-H. Chang, W.-Y. Chiu, and T.-Y. Hsieh, “Multipoint fuzzy prediction for load forecasting in green buildings,” in Proc. Int. Conf. Control, Automation Syst., HICO, Gyeongju, South Korea, Oct. 2016, pp. 562–567. (Student Best Paper Award)

9.        W.-L. Hsiao and W.-Y. Chiu, “Spectrum sensing control for enabling cognitive radio based smart grid,” in Proc. Int. Conf. Intell. Green Building Smart Grid, Prague, Czech Republic, Jun. 2016, pp. 1–6.

10.    J. Jiang, H. Sun, and W.-Y. Chiu, “Energy efficient massive MIMO system design for smart grid communications,” in Proc. IEEE Int. Conf. Commun. Workshops, Kuala Lumpur, Malaysia, May 2016, pp. 337–341. (top-tier conference in communications engineering)

11.    W.-Y. Chiu, H. Sun, and H. V. Poor, “An H∞ design for dynamic pricing in the smart grid,” in Proc. Asian Control Conf., Sabah, Malaysia, May 2015, pp. 1–6. 

12.    J.-T. Hsieh and W.-Y. Chiu, “Implementation of a transparent power information system on campus using existing infrastructures,” in Proc. IEEE Veh. Technol. Conf. Workshops, Glasgow, Scotland, May 2015, pp. 1–4.

13.    Y.-W. Chen and W.-Y. Chiu, “A framework for a consumer-end energy management system in smart grid,” in Proc. IEEE Global Conf. Consumer Electron., Osaka, Japan, Oct. 2015, pp. 101–103.

14.    W.-Y. Chiu, “A multiobjective approach to resource management in smart grid,” in Proc. Int. Conf. Control, Automation Inform. Sci., Gwangju, South Korea, Dec. 2014, pp. 182–187.

15.    W.-Y. Chiu, H. Sun, and H. V. Poor, “Demand-s​ide energy storage system management in smart grid,” in Proc. IEEE Int. Conf. Smart Grid Commun., Tainan City, Taiwan, Nov. 2012, pp. 73–78.

16.    W.-Y. Chiu, H. Sun, and H. V. Poor, “Robust power flow control in smart grids with fluctuatin​g effects,” in Proc. IEEE Int, Conf. Comput. Commun. Workshops, Orlando, Florida, USA, Mar. 2012, pp. 97–102. (top-tier conference in computer engineering)

 

System and Control

17.    S. H. Manoharan, W.-Y. Chiu, and C.-Y. Yu, “Dynamic leader selection of a multirobot system in multiple maze-like environments using reinforcement learning,” in Proc. IEEE Int. Conf. Appl. Syst. Innovation, Chiba, Japan, Apr. 2023, pp. x–y.

18.    Y.-L. Song, W.-Y. Chiu, and S. H. Manoharan, “Mixed compositional pattern-producing network-neuroevolution of augmenting topologies method for the locomotion control of a snake-like modular robot,” in Proc. SICE International Symp. Control Syst., Kusatsu, Japan, Mar. 2023, pp. 93–100.

19.    A. M. Romanov, V. D. Yashunskiy, and W.-Y. Chiu, “SABER: Modular reconfigurable robot for industrial applications,” in Proc. IEEE Int. Conf. Automation Science Engineering, Lyon, France, Aug. 2021, pp. 53–59.

20.    S. H. Manoharan and W.-Y. Chiu, “Consensus based formation control of automated guided vehicles using dynamic destination approach,” in Proc. Annu. Conf. Society of Instrument and Control Engineers of Japan, Hiroshima, Japan, Sep. 2019, pp. 902–907.

21.    C.-Y. Yu and W.-Y. Chiu, “Strategy for multirobot navigation on complicated terrains,” in Proc. IEEE Int. Conf. Appl. Syst. Innovation, Sapporo, Japan, May 2017, pp. 1782–1784. (Best Conference Paper Award)

22.    H.-Y. Yang and W.-Y. Chiu, “Cooperative multirobot strategy for seeking multiple sources,” in Proc. IEEE Int. Conf. Appl. Syst. Innovation, Sapporo, Japan, May 2017, pp. 1779–1781. (First Prize Paper Award)

23.    L.-H. Chang and W.-Y. Chiu, “Construction of a multirobot exploration system,” in Proc. IEEE Global Conf. Consumer Electron., Kyoto, Japan, Oct. 2016, pp. 1–2.

24.    Y.-W. Chen and W.-Y. Chiu, “Optimal robot path planning system by using a neural network-based approach,” in Proc. CACS Int. Automatic Control Conf., Ilan, Taiwan, Nov. 2015, pp. 85–90.

25.    W.-Y. Chiu, “Pareto optimal controller designs in differential games,” in Proc. CACS Int. Automatic Control Conf., Kaohsiung, Taiwan, Nov. 2014, pp. 179–184.

 

Signal and System

26.    T.-Y. Huang, H.-C. Chen, W.-Y. Chiu, and C.-H. Huang, “A smartphone-based design of wireless human-on-the-bike monitoring system,” in Proc. IEEE Global Conf. Consumer Electron., Kyoto, Japan, Oct. 2016, pp. 1–2.

27.    T.-Y. Huang and W.-Y. Chiu, “Systematic framework for solving real-world problems with multiple objectives,” in Proc. IEEE Global Conf. Consumer Electron., Kyoto, Japan, Oct. 2016, pp. 1–2.

28.    T.-K. Juan, W.-Y. Chiu, and S.-G. Chen, “A framework for an automatic program detection system,” in Proc. IEEE Global Conf. Consumer Electron., Osaka, Japan, Oct. 2015, pp. 104–105.  

29.    W.-Y. Chiu and B.-S. Chen, “Locating mobiles in general urban areas using combined convex optimization and weight-product tracking method,” in Proc. Int. Conf. Wireless Commun. Mobile Comput., Leipzig, Germany, Jun. 2009, pp. 1085–1090.

 

D. Chinese Periodical Papers

1.        關昊亮、王進華、邱偉育，「利用多目標最優化方法控制智能电網中電動汽車充電速率」，電气技术2017年18卷12期（Dec. 2017, pp. 76-80 元智大學、福州大學雙聯學位指導）。

2.        謝瑞廷、邱偉育、魏榮宗、陳文瑞、吳建明，「校園需量反應策略與浮動電價實現」，台電工程月刊第819期11月號（Nov. 2016, pp. 40–51）。

 

E. Monographs

1.        邱偉育，強化學習導論（電子書，出版日期：2021/04/22）。[程式碼下載]

2.        邱偉育，強化學習導論（全華圖書，出版日期：2021/11/22，ISBN 9789865038717）。

 

F. Patents

1.        范嘉豪、邱偉育。基於聯邦強化學習的邊緣計算卸載優化方法及通信系統。中華民國專利I792784,，公告於2023年2月11日，專利權止日2041年12月19日。Chia-Hao Fan and Wei-Yu Chiu, 2022, Method and system for federated reinforcement learning based offloading optimization in edge computing, Republic of China Patent I792784, and issued February 11, 2023.

2.        宋宇倫、邱偉育。多目標神經網路演化方法及裝置。中華民國專利I783594，公告於2022年11月11日，專利權止日2041年7月25日。Yu-Lun Song and Wei-Yu Chiu, 2022, Multi-objective neural network evolution method and apparatus, Republic of China Patent I783594, and issued November 11, 2022.

3.        邱崑晏、邱偉育。使用多智能體遷移式強化學習的再生能源競價方法。中華民國專利I 779732，公告於2022年10月1日，專利權止日2041年7月20日。Kun-Yen Chiu and Wei-Yu Chiu, 2022, Method for renewable energy bidding using multiagent transfer reinforcement learning, Republic of China Patent I779732, and issued October 1, 2022.

4.        邱偉育、陳頌仁。基於強化學習的棒球策略規劃方法及裝置。中華民國專利I779301，公告於2022年10月1日，專利權止日2040年6月25日。Song-Jen Chen and Wei-Yu Chiu, 2022, Method and apparatus for baseball strategy planning based on reinforcement learning, Republic of China Patent I779301, and issued October 1, 2022.

5.        江坤諺，邱偉育。基於強化學習的充電站能源使用規劃方法及裝置。中華民國專利I767868，公告於2022年6月11日，專利權止日2041年1月19日。Kun-Yan Jiang and Wei-Yu Chiu, 2022, Method and apparatus for planning energy usage of charging station based on reinforcement learning, Republic of China Patent I767868, and issued June 11, 2022.

6.        莊喻捷、邱偉育。基於強化學習的再生能源配置方法及裝置。中華民國專利I767525，公告於2022年6月11日，專利權止日2041年1月19日。Yu-Chieh Chuang and Wei-Yu Chiu, 2022, Method and apparatus for renewable energy allocation based on reinforcement learning, Republic of China Patent I767525, and issued June 11, 2022.

7.        黃粲博、邱偉育。基於強化學習的點對點能源共享方法及裝置。中華民國專利I763087，公告於2022年5月1日，專利權止日2040年10月20日。Tsan-Po Huang and Wei-Yu Chiu, 2022, Method and Apparatus for peer-to-peer energy sharing based on reinforcement learning, Republic of China Patent I763087, and issued May 1, 2022.

8.        邱偉育、陳頌仁。利用多目標強化學習之能源管理裝置及方法。中華民國專利I762128，公告於2022年4月21日，專利權止日2040年12月29日。Wei-Yu Chiu and Song-Jen Chen, 2022, Device and method of energy management using multi-objective reinforcement learning, Republic of China Patent I762128, and issued April 21, 2022.

9.        邱偉育、陳家銘。電力管理裝置及基於多目標最佳化的電力管理方法。中華民國專利I744179，公告於2021年10月21日，專利權止日2041年1月26日。Wei-Yu Chiu and Chia-Ming Chen, 2021, Power management device and power management method based on multiobjective optimization, Republic of China Patent I744179, and issued October 21, 2021. 

10.    莊智麟、邱偉育。基於基因演算法的資源分配方法及資料控制中心。中華民國專利I732350，公告於2021年7月1日，專利權止日2039年11月19日。Chih-Lin Chuang and Wei-Yu Chiu, 2021, Resource allocation method and data control center based on genetic algorithm, Republic of China Patent I732350, and issued July 1, 2021. 

11.    邱偉育、蔡松佑。無線電力驅動通訊網路的強化學習通訊時間分配方法及基地台。中華民國專利I714496，公告於2020年12月21日，專利權止日2040年4月12日。Wei-Yu Chiu and Sung-Yu Tsai, 2020, Communication time allocation method using reinforcement learning for wireless powered communication network and base station, Republic of China Patent I714496, and issued December 21, 2020.

12.    邱偉育、胡展維。基於強化學習的能源競價方法及裝置。中華民國專利I687890，公告於2020年3月11日，專利權止日2039年05月12日。Wei-Yu Chiu and Chan-Wei Hu, 2020, Method and apparatus for reinforcement learning based energy bidding, Republic of China Patent I687890, and issued March 11, 2020.

13.    W.-Y. Chiu and S.-J. Tsia, Communication time allocation method using reinforcement learning for wireless powered communication network and based station, US Patent, Patent No.: US11323167B2. (May 3, 2022–Dec. 25, 2040) 

14.    W.-Y. Chiu and C.-W. Hu, Method and apparatus for reinforcement learning based energy bidding, US Patent, Patent No.: US11063434 B2. (July 13, 2021–Nov. 27, 2039)

 

G. Guest Editorials

1.        W.-Y. Chiu, H. Sun, C. Wang, and A. V. Vasilakos, “Computational Intelligence for Smart Energy Applications to Smart Cities,” IEEE Trans. Emerg. Topics Comput. Intell., vol. 3, no. 3, pp. 173–176, Jun. 2019. [Table of Contents, Editorial]

2.        W.-Y. Chiu, H. Sun, J. Thompson, K. Nakayama, and S. Zhang, “IoT and Information Processing in Smart Energy Applications,” IEEE Commun. Mag., vol. 55, no. 10, pp. 44, Oct. 2017. [Table of Contents, Editorial]

3.        W.-Y. Chiu, H. Sun, J. Thompson, K. Nakayama, and S. Zhang, “Integrated communications, control, and computing technologies for enabling autonomous smart grid,” IEEE Commun. Mag., vol. 54, no. 12, pp. 58–59, Dec. 2016. [Table of Content, Editorial]

 

H. Note

* Open Access papers: [J14], [J19]

* Our papers [J8] and [C3] were cited by a Wikipedia article: Energy Demand Management, Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Energy_demand_management#cite_ref-3. Accessed: Feb. 3, 2016. [zip]

* Online articles cited in my papers: [J16] [89]; [J11] [32], [33]; [C6] [9], [10]