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1. Utilizing the Particle Swarm Optimization Algorithm for Determining Control Parameters for Civil Structures Subject to Seismic Excitation

   https://doi.org/10.3390/a14100292  

   Peckens, Courtney A.; Alsgaard, Andrea; Fogg, Camille; Ngoma, Mary C.; Voskuil, Clara (October 2021, Algorithms)

   Structural control of civil infrastructure in response to large external loads, such as earthquakes or wind, is not widely employed due to challenges regarding information exchange and the inherent latencies in the system due to complex computations related to the control algorithm. This study employs front-end signal processing at the sensing node to alleviate computations at the control node and results in a simplistic sum of weighted inputs to determine a control force. The control law simplifies to U = WP, where U is the control force, W is a pre-determined weight matrix, and P is a deconstructed representation of the response of the structure to the applied excitation. Determining the optimal weight matrix for this calculation is non-trivial and this study uses the particle swarm optimization (PSO) algorithm with a modified homing feature to converge on a possible solution. To further streamline the control algorithm, various pruning techniques are combined with the PSO algorithm in order to optimize the number of entries in the weight matrix. These optimization techniques are applied in simulation to a five-story structure and the success of the resulting control parameters are quantified based on their ability to minimize the information exchange while maintaining control effectiveness. It is found that a magnitude-based pruning method, when paired with the PSO algorithm, is able to offer the most effective control for a structure subject to seismic base excitation. 

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2. Performance of Response Based One Shot Controls Handling Missing Phasor Measurements

   https://doi.org/10.1109/PESGM41954.2020.9281396  

   Dahal, Niraj; Rovnyak, Steven M. (August 2020, 2020 IEEE Power & Energy Society General Meeting (PESGM))

   null (Ed.)

   With the advent of real-time PMU data acquisition technology, the possibility of solutions to several instability problems in power system has increased. However, PMUs may undergo different data quality issues like recording bad data or missing data. Some paper mentions about 5-10% of missing samples in some historical PMU's dataset. This paper assumes 0-10% of missing phasor samples by randomly deleting measurements and explores imputation methods of handling missing data in real time. The simulation is carried out in a DT-based stability prediction and one-shot control scheme of WECC's 176-bus model. Several control performances are evaluated to decide a useful method of missing data recovery for the response based one shot control scheme. A PMU data quality issue is not limited to missing samples only but also interference with noises. Later part of this paper performs simulation considering noisy phasor measurements. A 45 dB of Gaussian distributed noise is deliberately added to phasor samples and simulation is performed with different DT indices and thresholds for real time stability prediction and control actuation. 

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3. Comparing Stochastic Optimization Methods for Multi-robot, Multi-target Tracking

   https://doi.org/10.1007/978-3-031-51497-5_27  

   Xin, Pujie; Dames, Philip (February 2024, Springer Nature Switzerland)

   This paper compares different distributed control approaches which enable a team of robots search for and track an unknown number of targets. The robots are equipped with sensors which have a limited field of view (FoV) and they are required to explore the environment. The team uses a distributed formulation of the Probability Hypothesis Density (PHD) filter to estimate the number and the position of the targets. The resulting target estimate is used to select the subsequent search locations for each robot. This paper compares Lloyd’s algorithm, a traditional method for distributed search, with two typical stochastic optimization methods: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). This paper presents novel formulations of PSO and SA to solve the multi-target tracking problem, which more effectively trade off between exploration and exploitation. Simulations demonstrate that the use of these stochastic optimization techniques improves coverage of the search space and reduces the error in the target estimates compared to the baseline approach. 

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4. A New Harmonic-based Protection structure for Meshed Microgrids

   https://doi.org/10.1109/PESGM.2018.8585807  

   Beheshtaein, Siavash; Cuzner, Rob; Savaghebi, Mehdi; Guerrero, Josep M. (August 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM))

   This paper presents a novel harmonic-based overcurrent relay which detects and isolates three-phase faults in a meshed microgrid. The harmonic signals are generated by two Distributed Generators (DGs) which each of them communicate with its adjacent DG. In the first step, a set of features are extracted from DG output signal and then fed to a Support Vector Machine (SVM) to detect occurrence of fault. Once the fault is detected, based on minimum voltage measured by DG, two closest DGs will recognize and these two DGs inject two distinct harmonics to activate harmonic-based relays. As each set of relays located at either beginning or end of each section is activated by current with specific frequency, these relays behave like directional relays without using voltage transformers. As a result, the proposed method is cost-effective solution. The optimum Time Dial Settings (TDSs) of these relays are obtained by solving a coordination problem with Particle Swarm Optimization (PSO) algorithm. Real-time results are taken by OPAL-RT to show the effectiveness of the proposed method for two different locations of fault in a meshed microgrid. 

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5. PartialFibers: An efficient method for predicting Drug-Drug Interactions

   Bumin, Aysegul; Huang, Kejun; Kahveci, Tamer (December 2023, Springer)

   Drug resistance is one of the fundamental challenges in modern medicine. Using combinations of drugs is an effective solution to counter drug resistance as is harder to develop resistance to multiple drugs simultaneously. Finding the correct dosage for each drug in the combination remains to be a challenging task. Testing all possible drug-drug combinations on various cell lines for different dosages in wet-lab experiments is infeasible since there are many combinations of drugs as well as their dosages yet the drugs and the cell lines are limited in availability and each wet-lab test is costly and time-consuming. Efficient and accurate in silico prediction methods are surely needed. Here we present a novel computational method, PartialFibers to address this challenge. Unlike existing prediction methods PartialFibers takes advantage of the distribution of the missing drug-drug interactions and effectively predicts the dosage of a drug in the combination. Our results on real datasets demonstrate that PartialFibers is more flexible, scalable, and achieves higher accuracy in less time than the state of the art algorithms. 

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