Dynamical Network Biomarkers (DNB) theory has recently emerged as a promising framework for the ultra-early detection of diseases, particularly when dealing with high-dimensional low-sample-size (HDLSS) data. Once such early warning signs are identified, timely intervention becomes essential to prevent the disease from progressing into irreversible states. From the perspective of control theory, such intervention aims to improve the system stability margin to avoid critical transitions, a process known as re-stabilization. Successful re-stabilization requires knowledge of the system parameters. However, the HDLSS nature of biological datasets poses significant challenges for precise system parameter identification. To address this issue, this study explores the application of the extended Kalman filter (EKF) and proposes a novel dual-loop EKF approach. In the inner-loop iteration, we simultaneously estimate both the system states and unknown parameters by augmenting them into a unified model and employing EKF with first-order linearization. Meanwhile, the outer loop iteratively refines these parameter estimates by reusing historical measurement data, eliminating the need for additional data collection. Numerical simulations on low-and high-dimensional systems demonstrate that the proposed dual-loop EKF method significantly improves parameter estimation accuracy compared to the traditional EKF method, highlighting its potential applicability in complex biological contexts.

Active distribution networks facilitating bidirectional power exchange with renewable energy resources are susceptible to cyberattacks due to the integration of a diverse array of cyber components. This study introduces a grid-level defense strategy aimed at enhancing attack resiliency based on distribution network planning. Our proposed framework imposes a security requirement into existing planning methodologies, ensuring that voltage deviation from its rated value remains within a tolerable range against dynamically and maliciously injected power at end-user nodes. Unfortunately, the formulated problem in its original form is intractable because it is an infinite-dimensional bilevel optimization problem over a function space. To address this complexity, we develop an equivalent transformation into a tractable form as a mixed-integer linear program leveraging linear dynamical system theory and graph theory. Notably, our investigation reveals that the severity of potential attacks hinges solely on the cumulative reactances over the path from the substation to the targeted node, thereby reducing the problem to a finite-dimensional problem. Further, the bilevel optimization problem is reduced to a single-level optimization problem by using a technique utilized in solving the shortest path problem. Through extensive numerical simulations conducted on a 54-node distribution network benchmark, our proposed methodology exhibits a noteworthy 29.3% enhancement in the resiliency, with a mere 2.1% uptick in the economic cost.

Driving on curved roads under black ice can be difficult and risky due to a lack of visibility, unanticipated loss of traction, and the curvature effect. This greatly impacts driving behavior and often requires drivers to brake and accelerate, causing extra energy consumption and skidding. To address this issue, in this paper, we propose an energy-efficient and safe (eco-safe) driving strategy (EDS) for a host car employing nonlinear model predictive control (NMPC) that incorporates information on road curvatures and black ice surface conditions. An objective function is formulated considering parameters that affect fuel economy and driving safety, and solved using a nonlinearly constrained optimization technique with a finite prediction horizon. The EDS produces optimum acceleration and velocity trajectories for the host car by utilizing its motion dynamical model, preceding vehicle states, and information on road curvatures and icy surface conditions. Microscopic traffic simulations evaluate the performance of the EDS on typical freeways. Numerical results demonstrate that the proposed NMPC-based EDS significantly mitigates the host car’s fuel usage and emissions compared to the conventional driving strategy (CDS) whilst maintaining dynamic safety gaps. Specifically, for transition curves and S-curves, the proposed EDS enhances fuel efficiency by 6.76% and 7.82%, respectively, while reducing emissions (CO2, HC, CO, and NOx) by 6.54%–21.06% and 7.29%–34.97%, respectively. Likewise, the time to collision (TTC) with the proposed EDS remains below 2.5 s for both icy roadways, assuring safety. Moreover, the proposed EDS reduces the host car’s braking force, thus enhancing skidding safety by reducing skidding risk on icy surfaces. The proposed system is deployable as an advanced driving assistance system (ADAS) for semi-autonomous mobility. Finally, we recommend a pertinent policy for the proposed system, specific to these critical road conditions.

This paper presents an optimal pre-signal-based traffic signal framework that enhances intersection traffic flows while enabling efficient driving behavior, suitable for traditional and automated vehicles (AVs). Specifically, we propose the inclusion of an auxiliary traffic signal at an early stop line located before the standard one at the intersection, which allows the leading vehicle to enter the intersection immediately at a green light with a higher speed, reducing the start-up loss time by activating the auxiliary green light slightly earlier than the main conventional traffic signal. The relative positioning of the early stop line is optimized numerically for effectively redesigning the signaling system, considering relevant safety issues, constraints, and objectives. The vehicle control system employs model predictive control (MPC) to minimize costs associated with velocity and acceleration while maintaining a safe following distance. Through microscopic traffic simulations, the performance of traditional and automated vehicles is analyzed within this new signal system. The findings indicate a marked improvement in average speed, travel duration, fuel efficiency, and idling time compared to conventional intersection traffic management.

Metabolic syndrome (MetS) is a subclinical disease, resulting in increased risk of type 2 diabetes (T2D), cardiovascular diseases, cancer, and mortality. Dynamical network biomarkers (DNB) theory has been developed to provide early-warning signals of the disease state during a preclinical stage. To improve the efficiency of DNB analysis for the target genes discovery, the DNB intervention analysis based on the control theory has been proposed. However, its biological validation in a specific disease such as MetS remains unexplored. Herein, we identified eight candidate genes from adipose tissue of MetS model mice at the preclinical stage by the DNB intervention analysis. Using Drosophila, we conducted RNAi-mediated knockdown screening of these candidate genes and identified vasa (also known as DDX4), encoding a DEAD-box RNA helicase, as a fat metabolism-associated gene. Fat body-specific knockdown of vasa abrogated high-fat diet (HFD)-induced enhancement of starvation resistance through up-regulation of triglyceride lipase. We also confirmed that DDX4 expressing adipocytes are increased in HFD-fed mice and high BMI patients using the public datasets. These results prove the potential of the DNB intervention analysis to search the therapeutic targets for diseases at the preclinical stage.

Early-warning signals (EWS) are crucial for predicting critical transitions (CTs) in complex systems. In high-dimensional network systems, dynamical network marker (DNM) theory has been developed to obtain EWS by detecting significant fluctuations in specific subnetworks immediately before a CT. Mathematically, DNM nodes are characterized as the non-zero elements of the right eigenvector corresponding to the dominant eigenvalue of the linearly approximated system. While DNM theory has demonstrated effectiveness, particularly in biological applications, conventional approaches are limited to monolayer networks and fail to account for hierarchical structures including cell-to-cell interactions. To address this limitation, we extend DNM theory to heterogeneous hierarchical networks, analyzing their behavior before CTs through both theoretical and numerical approaches. Our findings reveal that stronger interactions necessitate a larger number of measured subnetworks but enable more precise identification of DNM nodes. These results highlight the critical role of sampling strategies in detecting CTs and contribute to a more comprehensive DNM theory for hierarchical networks.

Diabetes progression is increasingly hypothesized to involve the breakdown of homeostasis-a biological feedback regulation-which may be associated with a bifurcation phenomenon. The dynamical network biomarker (DNB) theory aims to detect qualitative state transitions associated with bifurcation phenomena by identifying DNB nodes whose fluctuations increase near critical points. However, identifying DNB nodes is challenging under high-dimensional and low-samplesize conditions. In this study, we focused on a biomolecular PID feedback control system-referred to as aPID-that exhibits saddle-node bifurcation, assuming that it underlies homeostatic regulation. We analytically show that the aPID controller state inherently satisfies DNB conditions at the bifurcation onset, enabling the a priori identification of DNB nodes without the need for exhaustive data-driven analysis.

This paper proposes an adaptive detection method for false data injection attacks based on the retraining of recurrent neural networks (RNNs). Machine learning-based detection methods heavily depend on historical pretraining data. Because of this dependency, the detection performance degrades when the power system largely fluctuates and becomes unexpected states. We propose retraining with new data obtained under unexpected states to avoid this serious degradation. To fasten the retraining, well-known RNNs such as long short-term memory (LSTM) and gated recurrent unit (GRU) should reduce the number of epochs, which prevents them from achieving high performance. To overcome this tradeoff, we utilize the echo state network (ESN), which is a representative model of Reservoir Computing. ESN significantly reduces training time compared with LSTM and GRU. We evaluate the proposed method with the three neural networks using the IEEE 68 bus system, confirming the performance of detection significantly degrades when the system enters an unexpected state. We also show the proposed retraining is effective in recovering the performance, and ESN outperforms the other RNNs in terms of retraining time.

This paper addresses the challenges of using High-Dimensional Low-Sample-Size (HDLSS) data set for early disease detection and re-stabilization in mRNA-protein gene regulatory networks. We demonstrate that detecting the pre-disease stage of mRNA-protein gene regulatory networks is possible using only the HDLSS data of either mRNA or protein. After detection, it is crucial to prevent disease progression at that point. This prevention can be achieved by enhancing the system’s stability, a process called re-stabilization. We demonstrate that the key nodes for re-stabilization in the mRNA-protein gene regulatory network manifest as mRNA- protein duals. The intervention strategy, whether suppression or promotion, is identical for each key dual’s mRNA and protein components. We further present a Lyapunov equation-based system identification method to estimate the system matrix using the HDLSS data set. The key nodes for re-stabilization can be identified from the estimated system matrix.

Ultra-early detection of diseases with High-Dimension Low-Sample-Size (HDLSS) data has been effectively addressed by the Dynamical Network Biomarkers (DNBs) theory. After ultra-early detection, it is crucial to consider ultra-early medical treatment for the detected disease. From the viewpoint of control engineering, ultra-early medical treatment is achieved by increasing the system’s stability and preventing the bifurcation, called re-stabilization. To implement effective re-stabilization, the system matrix is necessary. However, the available data in biological systems are often HDLSS, which is insufficient to identify the system matrix. In this paper, to realize HDLSS-based ultra-early medical treatment, we investigate an HDLSS data-based system matrix estimation method. First, HDLSS data is applied to compute the sample covariance matrix of the steady state. By assuming that the system matrix is sparse and the structure of the system matrix is known, it can utilize the Lyapunov equation to estimate the system matrix from the covariance matrix. The Lyapunov equation-based method gives a unique optimal estimation if the covariance matrix is full-rank. Otherwise, the optimal estimation is not unique. The sample covariance matrix computed from the HDLSS data is not full-rank. Thus, we apply shrinkage estimation to overcome the under-determined issue to obtain a well-conditioned covariance matrix with full rank. In addition, we confirm the effectiveness of the proposed method through numerical simulations.

Traffic accidents often result in quick bottlenecks and increase injudicious lane changes near incidents (or lane blockage), worsening collision risks, congestion, and fuel consumption. As a practical solution, this paper proposes a novel cooperative look-ahead lane change (Co-LLC) system for automated vehicles (AVs) to mitigate sudden accident-induced traffic effects by improving driving intelligence and safety in critical scenarios. The proposed Co-LLC system comprises the state prediction model, safety and impact evaluation unit, and decision system. Firstly, we analyze the immediate impacts of traffic accidents and identify that a lack of anticipation causes a lane change hot spot near the incident. Consequently, most attempts to change lanes early are unsuccessful due to uncooperative behavior from vehicles in the destination lane. Secondly, we design anticipatory and cooperative lane change systems for AVs to decide the need and feasibility of a lane change in advance. Thus, the proposed system enables AVs to change lanes smoothly and cooperate with other vehicles during lane changes. Finally, we investigate the impact of different penetration rates of AVs using the proposed system on overall traffic performance. The performance of our proposed system is compared to the traditional driving system, and the results show that our proposed system improves the lane-changing behavior of AVs, assists traditional vehicles in changing their lanes smoothly, and mitigates sudden accident-induced traffic impacts. Moreover, the proposed system improves the overall traffic performance with increased penetration rates. Our proposed system is computationally efficient and suitable for real-time driving in critical traffic scenarios.

Human driving behavior significantly affects vehicle fuel economy and emissions on hilly roads. This paper presents an ecological (eco) driving scheme (EDS) on hilly roads using nonlinear model predictive control (NMPC) in a mixed traffic environment. A nonlinear optimization problem with a relevant prediction horizon and a cost function is formulated using variables impacting the fuel economy of vehicles. The EDS minimizes vehicle fuel usage and emissions by generating the optimum velocity trajectory considering the longitudinal motion dynamics, the preceding vehicle’s state, and slope information from the digital road map. Furthermore, the immediate vehicle velocity and angle of the road slope are used to tune the cost function’s weight utilizing fuzzy inference methods for smooth maneuvering on slopes. Microscopic traffic simulations are used to show the effectiveness of the proposed EDS for different penetration rates on a real hilly road in Fukuoka City, Japan, in a mixed traffic environment with the conventional (human-based) driving scheme (CDS). The results show that the fuel consumption and emissions of vehicles are significantly reduced by the proposed NMPC-based EDS compared to the CDS for varying penetration rates. Additionally, the proposed EDS significantly increases the average speed of vehicles on the hilly road. The proposed scheme can be deployed as an advanced driver assistance system (ADAS).

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.

When a mathematical model is not available for a dynamical system, it is reasonable to use a data-driven approach for analysis and control of the system. With this motivation, the authors have recently developed a data-driven solution to Lyapunov equations, which uses not the model but the data of several state trajectories of the system. However, the number of state trajectories to uniquely determine the solution is O(n2) for the dimension n of the system. This prevents us from applying the method to a case with a large n. Thus, this paper proposes a novel class of data-driven Lyapunov equations, which requires a smaller amount of data. Although the previous method constructs one scalar equation from one state trajectory, the proposed method constructs three scalar equations from any combination of two state trajectories. Based on this idea, we derive data-driven Lyapunov equations such that the number of state trajectories to uniquely determine the solution is O(n).

Dynamical Network Marker (DNM) theory offers an efficient approach to identify warning signals at an early stage for impending critical transitions leading to system deterioration in extensive network systems, utilizing High-Dimension Low-Sample-Size (HDLSS) data. It is crucial to explore strategies for enhancing system stability and preventing critical transitions, a process known as re-stabilization. This paper aims to provide a theoretical basis for re-stabilization using HDLSS data by proposing a computational method to approximate pole placement for re-stabilizing large-scale networks. The proposed method analyzes HDLSS data to extract pertinent information about the network system, which is then used to design feedback gain and input placement for approximate pole placement. The novelty of this method lies in adjusting only the diagonal elements of the system matrix, thus simplifying the re-stabilization process and enhancing its practicality. The method is applicable to systems experiencing either saddle-node bifurcation or Hopf bifurcation. A theoretical analysis was performed to examine the perturbation of the maximum eigenvalues of the system matrix using the proposed approximate pole placement method. We validated the proposed method via simulations based on the Holme-Kim model.

This paper presents a cooperative intelligent driving (CID) scheme to optimally control a vehicle’s speed in multi-lane traffic, smooth its flow, and facilitate others to improve their performance. Under the scheme, lane-wise traffic speeds along the road, in the form of a road-speed profile (RSP), are dynamically estimated using information from connected vehicles (CVs) that broadcast their states. The driving decision under the scheme is computed in a model predictive control (MPC) framework that optimizes the vehicle’s acceleration to equalize traffic speeds across the lanes in a cooperative approach besides attaining the objective of safe and smooth driving. The optimization problem in the scheme is solved using a real-time computation method. The scheme is assessed by implementing it on a small portion of vehicles in typical freeway traffic affected by lane blocks or merging flows using the AIMSUN traffic simulator. It is found that low penetration of CID can relieve bottlenecks, harmonize the flow over lanes, and significantly improve overall traffic performance.

Energy consumption and emissions of a vehicle are highly influenced by road contexts and driving behavior. Especially, driving on horizontal curves often necessitates a driver to brake and accelerate, which causes additional fuel consumption and emissions. This paper proposes a novel optimal ecological (eco) driving scheme (EDS) using nonlinear model predictive control (MPC) considering various road contexts, i.e., curvatures and surface conditions. Firstly, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function based on factors affecting fuel consumption, emissions, and driving safety. Secondly, the EDS dynamically computes the optimal velocity trajectory for the host vehicle considering its dynamics model, the state of the preceding vehicle, and information of road contexts that reduces fuel consumption and carbon emissions. Finally, we analyze the effect of different penetration rates of the EDS on overall traffic performance. The effectiveness of the proposed scheme is demonstrated using microscopic traffic simulations under dense and mixed traffic environment, and it is found that the proposed EDS substantially reduces the fuel consumption and carbon emissions of the host vehicle compared to the traditional (human-based) driving system (TDS), while ensuring driving safety. The proposed scheme can be employed as an advanced driver assistance system (ADAS) for semi-autonomous vehicles.

In setpoint control, an equilibrium of the system to be controlled is typically employed as the setpoint. Thus, it is important to know the “possible equilibria” of the system, i.e. the value at which the state continues to stay by a suitable control input. Here, a possible equilibrium is called a controllable equilibrium. In this paper, we study two problems on controllable equilibria. First, we consider the problem of determining the set of controllable equilibria associated with constant inputs of magnitude one or less. Second, we address the problem of finding a constant input minimizing the distance between the resulting equilibrium and the desired state value. For each problem, we provide solutions in model-based and data-driven manners.

We consider a day-ahead scheduling problem for resources based on photovoltaic (PV) generation and demand profile predictions. Because the predicted profiles contain uncertainty, the set of profiles is represented as a confidence interval. Giving the predicted profile as a confidence interval, we consider the problem of obtaining ranges for the optimal operating profiles of storage batteries and thermal power plants. This corresponds to finding the region of possible solutions for all parameters representing PV/demand predictions. In order to find the exact solution region efficiently, we focus on the monotonicity of the solution. In particular, we aim to clarify what kind of optimization problem possesses monotonicity. As a first step, we have performed monotonicity analysis in various settings so far. In this study, we show that the problem, where the network structure is taken into account, has monotonicity under conditions with a theoretical proof. We also confirm its practical significance with a numerical example.

There exists a critical transition before dramatic deterioration of a complex dynamical system. Recently, a method to predict such shifts based on High‐Dimension Low‐Sample‐Size (HDLSS) data has been developed. Thus based on the prediction, it is important to make the system more stable by feedback control just before such critical transitions, which we call re‐stabilization. However, the re‐stabilization cannot be achieved by traditional stabilization methods such as pole placement method because the available HDLSS data is not enough to get a mathematical system model by system identification. In this article, a model‐free pole placement method for re‐stabilization is proposed to design the optimal input assignment and feedback gain for undirected network systems only with HDLSS data. The proposed method is validated by numerical simulations.