Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

Autonomic QoS Management Mechanism in Software Defined Network

With the increase of network complexity,the flexibility of network control and management becomes a nontrivial problem.Both Software Defined Network(SDN) and Autonomic Network technologies are sophisticated technologies for the network control and management.These two technologies could be combined together to construct a software defined self-managing solution for the future network.An autonomic QoS management mechanism in Software Defined Network(AQSDN) is proposed in this paper.In AQSDN,the various QoS features can be configured autonomically in an OpenFlow switch through extending the OpenFlow and OF-Config protocols.Based on AQSDN,a novel packet context-aware QoS model(PCaQoS) is also introduced for improving the network QoS.PCaQoS takes packet context into account when packet is marked and managed into forwarding queues.The implementation of a video application's prototype which evaluates the self-configuration feature of the AQSDN and the enhancement ability of the PCaQoS is presented in order to validate this design.

Towards Cloud Management by Autonomic Manager Collaboration

The management of clouds comprised of hundreds of hosts and virtual machines present challenging problems to administrators in ensuring that performance agreements are met and that resources are efficiently utilized. Automated approaches can help in managing such environments. Autonomic managers using policy-based management can provide a useful approach to such automation. We outline how collections of collaborating autonomic managers in cloud can be a step towards better management of clouds. We describe how a hierarchy of policy-based autonomic managers can collaborate using messages. The messages and when to communicate is inferred automatically from the policies given to the managers. We evaluate the approach via a prototype inspired by a cloud virtualized infrastructure and show how collaboration between managers in a hierarchy can improve the response time of a web server and avoid service level agreement violations. Results of three different scenarios shows the importance of collaboration between managers at different authority levels and how this collaboration can help to increase efficiency of current infrastructures.

An autonomic joint radio resource management algorithm in end-to-end reconfigurable system

This paper presents the multi-step Q-learning(MQL)algorithm as an autonomic approach to thejoint radio resource management(JRRM)among heterogeneous radio access technologies(RATs)in theB3G environment.Through the'trial-and-error'on-line learning process,the JRRM controller can con-verge to the optimized admission control policy.The JRRM controller learns to give the best allocation foreach session in terms of both the access RAT and the service bandwidth.Simulation results show that theproposed algorithm realizes the autonomy of JRRM and achieves well trade-off between the spectrum utilityand the blocking probability comparing to the load-balancing algorithm and the utility-maximizing algo-rithm.Besides,the proposed algorithm has better online performances and convergence speed than theone-step Q-learning(QL)algorithm.Therefore,the user statisfaction degree could be improved also.

Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections:A Review

Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless communication advances,vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention.In this paper,the recent studies on the planning and decision-making technologies at intersections are primarily overviewed.The general planning and decision-making approaches are presented,which include graph-based approach,prediction base approach,optimization-based approach and machine learning based approach.Since connected autonomous vehicles(CAVs)is the future direction for the automated driving area,we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies.Both four-way signalized and unsignalized intersection(s)are investigated under purely automated driving traffic and mixed traffic.The study benefit from current strategies,protocols,and simulation tools to help researchers identify the presented approaches’challenges and determine the research gaps,and several remaining possible research problems that need to be solved in the future.

A hydrothermal investigation system for the Qianlong-Ⅱ autonomous underwater vehicle

Qianlong-Ⅱ is a fully autonomous underwater vehicle designed for the investigation of submarine resources,particularly polymetallic sulfides. It was used to successfully explore hydrothermal fields on the Southwest Indian Ridge. Here, we summarized the exploration of hydrothermal systems using Qianlong-Ⅱ, including detailed descriptions of its implementation along with the systems used for data management and fast mapping. We also introduced a method to remove platform magnetic interference using magnetic data while Qianlong-Ⅱ is spinning. Based on hydrothermal anomalies collected by Qianlong-Ⅱ, we developed a rapid method for locating hydrothermal vents. Taking one dive as an example, we systemically demonstrated the process for analyzing hydrothermal survey data to locate hydrothermal vents.

MicroGrid Designer:user-friendly design,operation and control assist tools for resilient microgrid and autonomous community

During this decade,many countries have experienced natural and accidental disasters,such as typhoons,floods,earthquakes,and nuclear plant accidents,causing catastrophic damage to infrastructures.Since the end of 2019,all countries of the world are struggling with the COVID-19 and pursuing countermeasures,including inoculation of vaccine,and changes in our lifestyle and social structures.All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous,so that vital lifelines are secured during calamities.A paradigm shift has been taking place toward reorganizing the energy social service management in many countries,including Japan,by effective use of sustainable energy and new supply schemes.However,such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service.Therefore,new social infrastructures and novel management systems to supply energy and social service will be required.In this paper,user-friendly design,operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance.

Dual Layered Architecture for Multi Agent Based Islanding and Load Management for Microgrids

This paper proposes a novel dual layered multi agent system (MAS) based control system for the use in microgrid operations. In developing a smarter grid capable of withstanding disturbances and/or outages and providing quality service to the consumers, reliable microgrid control architecture is vital. The innovative microgrid control system proposed, makes the microgrid capable of isolating the local grid from effects of any upstream disturbances in the main utility grid by operating disconnected from the main utility via islanding, and it allows the most critical local loads to be supplied by any, available, local power source during such islanded operation. The proposed MAS control architecture is developed using the JADE platform and it is used to control a test network simulated in MATLAB. The results of these simulations show the capability of developing MAS based reliable control mechanism for islanding and load management of microgrids based on the proposed concept.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

Autonomous Vehicle Design in Lean Product Development Processes for Value Stream Map

The global automotive industry is giving a difficult and common test in order to create advanced life models in the near future plans or scenarios that include current autonomous vehicle technologies. Therefore, the main purpose of the research is to comparatively evaluate the impact of autonomous vehicle technologies, which are newly included in the automotive manufacturing industry under sustainable competition, on lean product development processes, value acquisition and preservation, in different organizational structures in the approach. Although mergers or brand acquisitions in the global automotive industry create joint R & D (Research Development) or joint new P & D (Product Development) process structures for the development of autonomous vehicle technologies, heavy competition continues in the market. These new processes create different needs for the merger and partnership of the renewed traffic infrastructures under national and international regulations, and for the implementation of the new autonomous life model. Firm and brand marriages, mergers or acquisitions in today’s automotive industry have ensured the high diffusion of lean product development processes under the stream of value creation or preservation carried out specific to the company under competition. Brand mergers in automotive industry companies struggling to survive under high competition create new work disciplines, professions, and engineering flow steps in lean product development processes. However, lean product development processes driven by technological innovation under simplification have resulted in the integration of parts and systems within the autonomous vehicle design structure, as well as creating new interdisciplinary value streams or different stakeholders. Therefore, the research revealed the significant effects of lean product development processes on the value stream in the automotive industry, on the mixed and lean product development process structure formed by new or existing vehicle systems (conventional vehicle) under the penetration of each existing and new discipline. This research compares the efficient operation steps of the process stakeholders in the autonomous vehicle design parts or systems containing innovation and new technology together with the value stream in the lean product development process, and the new process stakeholder’s business-oriented global and local automotive industry companies. New autonomous vehicle technologies, together with their unique software, hardware and development analysis, have been involved in the lean product development process with their interdisciplinary studies or expertise. Therefore, the study firstly focused on the technologies in environmental use together with the new basic features of autonomous vehicles, and then examined in depth the new or existing disciplines and interdisciplinary basic structure that these innovations affect under the value stream in the lean product development process. In addition, micro-level results and recommendations were shared, shedding light on how autonomous vehicle levels will create changes in the new product development process.

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.

Shaping the Next Generation Pharmaceutical Supply Chain Control Tower with Autonomous Intelligence

This paper summarizes the findings of an industry panel study evaluating how new Autonomous Intelligence technologies,such as artificial intelligence and machine learning,impact the system and operational architecture of supply chain control tower (CT) implementations that serve the pharmaceutical industry.Such technologies can shift CTs to a model in which real-time information gathering,analysis,and decision making are possible.This can be achieved by leveraging these technologies to better manage decision complexity and execute decisions at levels that cannot otherwise be managed easily by humans.Some of the key points identified are in the areas of the fundamental capabilities that need to be supported and the improved level of decision visibility that they provide.We also consider some the challenges in achieving this,which include data quality and integrity,collaboration and data sharing across supply chain tiers,cross-system interoperability,decision-validation and organizational impacts,among others.

Autonomous Power Management for Interlinked AC-DC Microgrids

The existing power management schemes for interlinked AC-DC microgrids have several operational drawbacks.Some of the existing control schemes are designed with the main objective of sharing power among the interlinked microgrids based on their loading conditions,while other schemes regulate the voltage of the interlinked microgrids without considering the specific loading conditions.However,the existing schemes cannot achieve both objectives efficiently.To address these issues,an autonomous power management scheme is proposed,which explicitly considers the specific loading condition of the DC microgrid before importing power from the interlinked AC microgrid.This strategy enables voltage regulation in the DC microgrid,and also reduces the number of converters in operation.The proposed scheme is fully autonomous while it retains the plug-nplay features for generators and tie-converters.The performance of the proposed control scheme has been validated under different operating scenarios.The results demonstrate the effectiveness of the proposed scheme in managing the power deficit in the DC microgrid efficiently and autonomously while maintaining the better voltage regulation in the DC microgrid.

Iaso： an autonomous fault-tolerant management system for supercomputers

With the increase of system scale, the inherent reliability of supercomputers becomes lower and lower. The cost of fault handling and task recovery increases so rapidly that the reliability issue will soon harm the usability of supercomputers. This issue is referred to as the ＂reliability wall＂, which is regarded as a critical problem for current and future supercomputers. To address this problem, we propose an autonomous fault-tolerant system, named Iaso, in MilkyWay- 2 system. Iaso introduces the concept of autonomous management in supercomputers. By autonomous management, the computer itself, rather than manpower, takes charge of the fault management work. Iaso automatically manage the whole lifecycle of faults, including fault detection, fault diagnosis, fault isolation, and task recovery. Iaso endows the autonomous features with MilkyWay-2 system, such as self-awareness, self-diagnosis, self-healing, and self-protection. With the help of Iaso, the cost of fault handling in supercomputers reduces from several hours to a few seconds. Iaso greatly improves the usability and reliability of MilkyWay-2 system.

东方红系列通信卫星控制技术发展综述

自1975年至今,我国已成功发展了以东方红系列为主的多型通信卫星平台,累计发射卫星70余颗,为我国国民经济和国防建设做出了重大贡献.概述我国东方红系列通信卫星平台的发展历程,梳理通信卫星控制系统发展脉络及主要特点,重点从姿态控制、轨道控制、信息体系架构和自主健康管理4个方面阐述了通信卫星控制技术主要进展.在此基础上,结合空间任务需求,对通信卫星控制技术的未来发展方向进行分析和展望,助推未来创新发展.

太阳系边际探测器自主技术需求分析

回顾了已有的太阳系边际探测任务及其自主运行技术的初步应用情况,归纳了太阳系边际探测任务的特点。结合发展和应用历程,分析了实现太阳系边际探测器自主运行所需的关键技术,并给出了各项技术的重点研究方向,包括不确定环境下的自主任务规划技术、超长周期高可靠性自主健康管理技术、极远深空环境自主导航技术、核电源系统自主运行管理技术、极端热环境下的自主热控技术和超远距离下自主通信与休眠技术,能够为太阳系边际探测任务的关键技术研究和任务实施提供参考。

推动自动驾驶运输服务应用的思考与对策

为推动自动驾驶汽车在道路运输市场实现商业化应用,引导自动驾驶运输服务健康有序发展,首先从道路运输行业管理的角度出发,分析制约自动驾驶汽车规模化应用的长尾问题,提出通过增加约束条件消除应用长尾的应对策略。然后,从车辆技术、线路使用、从业人员、企业管理、行业监管等5个方面,提出与现阶段自动驾驶技术相匹配且符合现行法律法规要求的自动驾驶汽车从事道路运输服务经营活动的使用条件,以有效解决自动驾驶技术应用的长尾问题。最后,分析自动驾驶汽车运行区域开放程度、车辆在区域内的运行速度、载客和载货量等要素与自动驾驶汽车商业化应用风险的关系,确定高、中、低3个风险等级下各要素的指标参数,并基于不同运输业态的风险等级提出自动驾驶汽车应用预期,为自动驾驶汽车商业化应用提出可行对策。

吸纳性规制:农村集体资产经营中的基层政府行为模式

在农村集体经济全面转向以集体土地和物业出租为主要形式的租赁型经济条件下,基层政府针对集体资产经营的行为模式需要更加深入的探讨。基于政府规制理论的演进脉络和场景变迁,提出“吸纳性规制”概念,用以分析基层政府主导集体资产经营的政策实践。经验研究发现,基层政府一方面完善集体资产持有、交易、使用等环节的具体规则,持续推动行政监管扩容;另一方面通过基层组织支配和集体资产代管,完成对集体资产经营权的接收。二者构成了吸纳性规制的不同实践层次,展现了行政权力的边界延展与行政系统的职能扩充。在这一过程中,基层政府注重寻求“合法性策略”与“实质性目标”之间的平衡,将规范经济秩序、提高经济绩效作为获致集体资产经营主导权的重要基础。吸纳性规制具有促进集体经济规范运行、提高集体收益的积极功能,但是也可能带给基层政府更多应对复杂市场环境、介入集体产权纠纷的压力。

基于层次聚类法对大学生自主学习能力评价的研究

层次聚类法是一种科学、客观的评价方法,从大学生的实际情况出发,应用层次聚类法,为“互联网+”环境下大学生自主学习能力建立一个评价准则。此准则经检验,对大学生自主学习能力的判断更为公平,也为高校评判学生自主学习能力提供依据,同时在指导学生个人自主学习能力提升上也有帮助。另外,层次聚类法可助力院校和教师开展大学生自主学习能力的培养工作,培养具有高效自主学习能力的人才。

果园智能化作业装备自主导航技术研究进展

果园生产管理主要包括喷药、施肥、割草、修剪、授粉、疏花和采收分级等作业环节,需要大量的人力投入,随着我国人口老龄化程度加剧,亟需果园生产管理由机械化向智能化转型升级。自主导航技术是果园机械化装备实现智能化的关键技术。本文围绕果园智能化作业装备导航控制需求,结合国内外研究现状,分别阐述了包含导航定位信息和障碍物信息的果园作业场景感知技术,导航地图构建、导航路径提取和路径规划技术,行走底盘运动学模型构建、运动控制技术,多机协同控制、远程交互控制技术等。随着智慧农业发展,智慧果园已成为果园未来发展方向,果园智能化作业装备是智慧果园建设必不可少的关键环节,在此基础上,归纳了我国果园智能化作业装备自主导航技术发展面临的问题为:环境感知能力不足、路径提取不稳定、局部路径规划不灵活、导航系统环境适应性欠缺、多机协同和远程控制不成熟等,提出了多传感器融合的环境感知与路径提取、完整路径规划、强通用性果园导航、大型果园多作业环节的多机协同与远程操作等未来发展方向。