arXiv subjects

Hassan Jafarzadeh

Publications and source records attributed to Hassan Jafarzadeh.

8 recordsLinked to original sources

arXiv2026

Functional requirements decomposition in set-based design

Designing systems is typically uncertain and ambiguous at early stages. Set-based design supports alternative exploration and gradual uncertainty reduction during the early lifecycle, making it practical for complex systems design. In parallel, the functional requirements decomposition helps to advance the design incrementally. However, current literature on set-based design lacks formal guidance in how to decompose functional requirements. To bridge this gap, we introduce a four-step method to decompose functional requirements for set-based design hierarchically. We systematically define, reason, and narrow the sets, breaking down the functional requirements into formal sub-requirements. This method allows parallel abstraction, ensuring the resulting system satisfies the top-level functional requirements.

eess.SY↗

arXiv2025

Correct-by-construction requirement decomposition

In systems engineering, accurately decomposing requirements is crucial for creating well-defined and manageable system components, particularly in safety-critical domains. Despite the critical need, rigorous, top-down methodologies for effectively breaking down complex requirements into precise, actionable sub-requirements are scarce, especially compared to the wealth of bottom-up verification techniques. Addressing this gap, we introduce a formal decomposition for contract-based design that guarantees the correctness of decomposed requirements if specific conditions are met. Our (semi-)automated methodology augments contract-based design with reachability analysis and constraint programming to systematically identify, verify, and validate sub-requirements representable by continuous bounded sets -- continuous relations between real-valued inputs and outputs. We demonstrate the efficacy and practicality of a correct-by-construction approach through a comprehensive case study on a cruise control system, highlighting how our methodology improves the interpretability, tractability, and verifiability of system requirements.

eess.SY↗

arXiv2024

GenGMM: Generalized Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation

Domain adaptive semantic segmentation is the task of generating precise and dense predictions for an unlabeled target domain using a model trained on a labeled source domain. While significant efforts have been devoted to improving unsupervised domain adaptation for this task, it is crucial to note that many models rely on a strong assumption that the source data is entirely and accurately labeled, while the target data is unlabeled. In real-world scenarios, however, we often encounter partially or noisy labeled data in source and target domains, referred to as Generalized Domain Adaptation (GDA). In such cases, we suggest leveraging weak or unlabeled data from both domains to narrow the gap between them, resulting in effective adaptation. We introduce the Generalized Gaussian-mixture-based (GenGMM) domain adaptation model, which harnesses the underlying data distribution in both domains to refine noisy weak and pseudo labels. The experiments demonstrate the effectiveness of our approach.

cs.CV↗

arXiv2021

Gaussian Process-based Model Predictive Controller for Connected Vehicles with Uncertain Wireless Channel

In this paper, we present a data-driven Model Predictive Controller that leverages a Gaussian Process to generate optimal motion policies for connected autonomous vehicles in regions with uncertainty in the wireless channel. The communication channel between the vehicles of a platoon can be easily influenced by numerous factors, e.g. the surrounding environment, and the relative states of the connected vehicles, etc. In addition, the trajectories of the vehicles depend significantly on the motion policies of the preceding vehicle shared via the wireless channel and any delay can impact the safety and optimality of its performance. In the presented algorithm, Gaussian Process learns the wireless channel model and is involved in the Model Predictive Controller to generate a control sequence that not only minimizes the conventional motion costs, but also minimizes the estimated delay of the wireless channel in the future. This results in a farsighted controller that maximizes the amount of transferred information beyond the controller's time horizon, which in turn guarantees the safety and optimality of the generated trajectories in the future. To decrease computational cost, the algorithm finds the reachable set from the current state and focuses on that region to minimize the size of the kernel matrix and related calculations. In addition, we present an efficient recursive approach to decrease the time complexity of developing the data-driven model and involving it in Model Predictive Control. We demonstrate the capability of the presented algorithm in a simulated scenario.

eess.SY↗

arXiv2021

DMPC: A Data-and Model-Driven Approach to Predictive Control

This work presents DMPC (Data-and Model-Driven Predictive Control) to solve control problems in which some of the constraints or parts of the objective function are known, while others are entirely unknown to the controller. It is assumed that there is an exogenous ``black box'' system, e.g. a machine learning technique, that predicts the value of the unknown functions for a given trajectory. DMPC (1) provides an approach to merge both the model-based and black-box systems; (2) can cope with very little data and is sample efficient, building its solutions based on recently generated trajectories; and (3) improves its cost in each iteration until converging to an optimal trajectory, typically needing only a few trials even for nonlinear dynamics and objectives. Theoretical analysis of the algorithm is presented, proving that the quality of the trajectory does not worsen with each new iteration, as well as providing bounds on the complexity. We apply the DMPC algorithm to the motion planning of an autonomous vehicle with nonlinear dynamics.

eess.SY↗

arXiv2019

Learning Model Predictive Control for Connected Autonomous Vehicles

A Learning Model Predictive Controller (LMPC) is presented and tailored to platooning and Connected Autonomous Vehicles (CAVs) applications. The proposed controller builds on previous work on nonlinear LMPC, adapting its architecture and extending its capability to (a) handle dynamic environments and (b) account for data-driven decision variables that derive from an unknown or unknowable function. The paper presents the control design approach, and shows how to recursively construct an outer loop candidate trajectory and an inner iterative LMPC controller that converges to an optimal strategy over both model-driven and data-driven variables. Simulation results show the effectiveness of the proposed control logic.

math.OC↗

arXiv2019

A Dynamic Sustainable Competitive Petroleum Supply Chain Model for Various Stakeholders with Shared Facilities

Petroleum industry is the world's biggest energy source, and its associated industries such as production, distribution, refining and retail are considered as the largest ones in the world. Having the increasing price and governments job creation and international environmental policies, the petroleum companies try to maximize the number of created job, and their profit and minimize the air pollution simultaneously. To meet these objectives, an effective detailed and precise planning is needed. On the other hand, the dynamic environment and the presence of various stakeholders add to the complexity of planning and design of petroleum supply chain. Therefore, the multi-period, multi-objective, multi-level and multi-product dynamic sustainable competitive petroleum supply chain (DSCPSC) model taking into consideration the various stakeholders have been proposed in this paper. The proposed model is an MILP model and GAMS 24.1.2 software has been used to run it for a part of real petroleum supply chain data. Sensitivity analysis was then performed to determine the sensitivity of the results to the variation of the coefficients in objective function. Sensitivity analysis reveals that the highest variations of the objective function were observed with respect to the variable costs, facility installation costs and pipeline transportation costs.

math.OC↗

arXiv2018

PAIM: Platoon-based Autonomous Intersection Management

With the emergence of autonomous ground vehicles and the recent advancements in Intelligent Transportation Systems, Autonomous Traffic Management has garnered more and more attention. Autonomous Intersection Management (AIM), also known as Cooperative Intersection Management (CIM) is among the more challenging traffic problems that poses important questions related to safety and optimization in terms of delays, fuel consumption, emissions and reliability. Previously we introduced two stop-sign based policies for autonomous intersection management that were compatible with platoons of autonomous vehicles. These policies outperformed regular stop-sign policy both in terms of average delay per vehicle and variance in delay. This paper introduces a reservation-based policy that utilizes the cost functions from our previous work to derive optimal schedules for platoons of vehicles. The proposed policy guarantees safety by not allowing vehicles with conflicting turning movement to be in the conflict zone at the same time. Moreover, a greedy algorithm is designed to search through all possible schedules to pick the best that minimizes a cost function based on a trade-off between total delay and variance in delay. A simulator software is designed to compare the results of the proposed policy in terms of average delay per vehicle and variance in delay with that of a 4-phase traffic light.

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