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The IEEE International Conference on Plasma Science (ICOPS) is held annually, sponsored by the Plasma Science and Applications Committee (PSAC) of the IEEE Nuclear and Plasma Science Society (NPSS). The 47th IEEE NPSS ICOPS was organized ONLINE from December 06 to 10, 2020, by the National Institute of Education, Nanyang Technological University, Singapore. The ICOPS2020 was co-organized together with the 2nd Asia-Pacific Conference on Plasma and Terahertz Science (APCOPTS) which is envisioned as the future Asian version of the ICOPS series of conferences. The ICOPS2020 was initially planned as an onsite physical conference from May 24 to 28, 2020, at Marina Bay Sands, Singapore. However, due to the COVID-19 pandemic, it was shifted to online virtual conference. The conference’s technical program included a wide range of plasma science and technology topics, which ranged from basic and fundamental research to traditional and emerging technology applications. Since the ICOPS2020 was co-organized with APCOPTS, it also had a very strong coverage of fields related to terahertz science and applications. A total of 627 presentations from scientists and participants from over 41 countries were made at ICOPS2020 with a record total registration of 754. All presentations were recorded and were available to the registered participants for 90 days after the conference was over. The program also included a women-in-engineering talk by Jackie Y. Ying from A* Star Singapore and a young professionals networking symposium on “Plasma Entrepreneurship” with the theme “Plasma entrepreneurship—Active translation of cutting-edge research to commercially viable product.” [ABSTRACT FROM AUTHOR]

Reorganizing bus frequencies to cater for the actual travel demands can significantly save the cost of the public transport system. Many, if not all, previous studies formulate this as a bus frequency optimization problem that tries to minimize passengers’ average waiting time. On the other hand, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an index-based $(1-1/e)$ (1 - 1 / e) -approximation algorithm. By exploiting the locality property of routes in a bus network, we further propose a partition-based greedy method that achieves a $(1-\rho)(1-1/e)$ (1 - ρ) (1 - 1 / e) approximation ratio. Then we propose a progressive partition-based greedy method to further boost the efficiency while achieving a $(1-\rho)(1-1/e-\varepsilon)$ (1 - ρ) (1 - 1 / e - ɛ) approximation ratio. For the FASTCO problem, two greedy-based heuristic methods are proposed. Experiments on a real city-wide bus dataset in Singapore have been conducted to verify the efficiency, effectiveness, and scalability of our methods in addressing FAST and FASTCO respectively. [ABSTRACT FROM AUTHOR]

Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic operation of a modern power system. In practice, due to various faulty issues in the sensor, communication, or database system, the historical and online measurement data may not be always complete, and the missing data could dramatically degrade the forecasting model's accuracy. To solve this problem, this paper proposes an integrated missing-data tolerant model for probabilistic PV power generation forecasting. Taking historical PV generations as input, this model is based on a recursive long short-term memory network (Rec-LSTM), which can provide multi-step ahead forecasting of the probability distribution of PV generation. The unobserved input data will be imputed recursively based on the model output at the previous time step. During the training process, the imputations and forecasting values are iteratively updated by the negative log-likelihood loss function. As a salient advantage, this method can deal with data missing scenarios at both offline and online stages. Numerical experiments are conducted on two one-year datasets from Australia and Singapore, respectively. Probabilistic forecasting for both large-scale and small-scale building-level PV power generation is tested at the time resolution of 15 mins. Testing results show the proposed method can achieve superior probabilistic prediction accuracy as well as strong robustness under various data missing scenarios, compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the recent technological innovation, unmanned aerial vehicles, known as drones, have found numerous applications including package and parcel delivery for shippers. Drone delivery offers benefits over conventional ground-based vehicle delivery in terms of faster speed, lower cost, more environment-friendly, and less manpower needed. However, most of existing studies on drone delivery planning and scheduling focus on a single shipper and ignore uncertainty factors. As such, in this paper, we consider a scenario that multiple shippers can cooperate to minimize their drone delivery cost. We propose the Bayesian Shipper Cooperation in Stochastic Drone Delivery (BCoSDD) framework. The framework is composed of three functions, i.e., package assignment, shipper cooperation formation and cost management. The uncertainties of drone breakdown and misbehavior of cooperative shippers are taken into account by using multistage stochastic programming optimization and dynamic Bayesian coalition formation game. We conduct extensive performance evaluation of the BCoSDD framework by using customer locations from Solomon benchmark suite and a real Singapore logistics industry. As a result, the framework can help the shippers plan and schedule their drone delivery effectively. [ABSTRACT FROM AUTHOR]

Over the past decade, cyberattack events on the electricity grid are rising and have severely impacted grid operation. These attacks are becoming more intelligent as they can falsify critical data and bypass existing protection protocols. This could result in the system to operate in a nonoptimal condition and suffer substantial economic losses over a prolonged period. Hence, it is crucial to develop tools to defend the system against such attacks to minimize the cost of malicious operations. This article aims to develop a novel command authentication strategy to detect and mitigate false data injection attacks (FDIAs) targeting the system centralized economic dispatch (ED) control signals. First, we describe the ED problem based on Singapore’s deregulated market context. We then perform a risk assessment and formulate a stealthy FDIA vector targeting the ED control signals. Subsequently, we propose a novel authentication technique leveraging on the multiagent system, which includes detection and mitigation algorithms to verify the ED control commands. Last, we evaluate the efficiency and latency of the proposed technique on the 5-bus, 3-generating unit, and 15-generating unit test system. Simulation and experiment results using a real-time hardware-in-the-loop system proves that the authentication technique can detect and mitigate the stealthy FDIA vector with high accuracy. [ABSTRACT FROM AUTHOR]

The data center market grows rapidly with the increase of data and its corresponding applications (e.g., machine learning, cloud storage, Internet of Things, and so on). The growth is boosted recently due to the shift of activities online during the COVID-19 pandemic. Reducing the energy consumption of data centers faces various challenges that are further aggravated by the tropical conditions with high temperature and humidity in the tropics like Singapore. The prevailing siloed approach of operating the information technology (IT) and the facility systems separately has resulted in wasteful over-provisioning. The recently proposed approaches for energy usage minimization under various constraints including thermal safety scale poorly with the data center size and often result in non-optimal solutions. To advance the state of the art, we apply deep reinforcement learning (DRL) to address the scalability problem and achieve optimality over a long time horizon in reducing data center energy usage. In particular, we deploy the data-driven deep model and physical rule based model in lieu of the physical data center during the training and validation phases to manage the thermal safety risks caused by DRL's strategy of learning from errors. [ABSTRACT FROM AUTHOR]