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On the Morality of Artificial Intelligence

IEEE TECHNOLOGY AND SOCIETY MAGAZINE 2020年第1期39卷 16-25页

作者： Luccioni, Alexandra Bengio, Yoshua Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada Univ Montreal Mila Quebec AI Inst Montreal PQ Canada

Examines ethical principles and guidelines that surround machine learning and artificial intelligence.

关键词： Ethics Artificial intelligence Guidelines Machine learning algorithm design and analysis

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The Spiral Optimization algorithm: Convergence Conditions and Settings

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第1期50卷 360-375页

作者： Tamura, Kenichi Yasuda, Keiichiro Tokyo Metropolitan Univ Dept Elect & Elect Engn Tokyo 1920397 Japan

The spiral optimization (SPO) algorithm proposed by Tamura and Yasuda is a relatively novel and simple search concept inspired by natural spiral phenomena. This algorithm searches continuous space using no gradient and only spiral trajectories composed of spiral vectors generated by deterministic spiral models. The primary purpose of this paper is to propose conditions and settings that mathematically ensure the convergence of the SPO algorithm to a stationary point. The conditions relating to the sizes and directions of the spiral vectors and the initial search points are based on direct search theory and recent SPO algorithm theories. The presented convergence was numerically verified using test functions with different properties.

关键词： Spirals Heuristic algorithms Convergence Trajectory algorithm design and analysis Optimization Linear programming Convergence direct search nature-inspired computation spiral optimization (SPO) algorithm stationary point

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Context-Aware Dynamic Asset Allocation for Maritime Interdiction Operations

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第3期50卷 1055-1073页

作者： Sidoti, David Han, Xu Zhang, Lingyi Avvari, Gopi Vinod Ayala, Diego Fernando Martinez Mishra, Manisha Sankavaram, Muni Sravanth Kellmeyer, David L. Hansen, James A. Pattipati, Krishna R. Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Two Sigma Modeling Dept New York NY 10013 USA Argus Informat & Advisory Serv LLC Data & Applicat Solut White Plains NY 10601 USA SPAWAR Syst Ctr Pacific Command & Control Dept San Diego CA 92152 USA US Naval Res Lab Marine Meteorol Div Monterey CA 93943 USA

This paper validates two approximate dynamic programming approaches on a maritime interdiction problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to interdict multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights. The asset allocation is based on a probability of activity surface, which represents spatio-temporal target activity obtained by integrating intelligence data on drug smuggler whereabouts/waypoints for contraband transportation, behavior models, and meteorological and oceanographic information. We validate the proposed architectural and algorithmic concepts via several realistic mission scenarios. We conduct sensitivity analyses to quantify the robustness and proactivity of our approach, as well as to measure the value of information used in the allocation process. The contributions of this paper have been transitioned to and are currently being tested by Joint Interagency Task Force-South, an organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans.

关键词： Resource management Dynamic programming Drugs Stochastic processes Transportation algorithm design and analysis Planning Approximate dynamic programming Gauss-Seidel iteration mission planning resource management problem rollout sensitivity analysis value of information (VOI)

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Steady-State Voltage Regulation With Reduced Photovoltaic Power Curtailment

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IEEE JOURNAL OF PHOTOVOLTAICS 2020年第6期10卷 1853-1863页

作者： Zhang, Zhanqiang Mishra, Yateendra Dou, Chunxia Yue, Dong Zhang, Bo Tian, Yu-Chu Yanshan Univ Sch Elect Engn Qinhuangdao 066044 Hebei Peoples R China Queensland Univ Technol Sch Elect Engn & Robot Brisbane Qld 4001 Australia Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia

The curtailment of photovoltaic (PV) power injection is one of the ways to mitigate the overvoltage issue in residential distribution networks with high levels of PVs. The reactive power compensation (RPC) of PV inverter, when applied in conjunction with the active power curtailment (APC) of battery energy storage system (BESS), is more effective in improving the voltage profile. Hence, a coordinated power compensation algorithm (PCA) is proposed in this article to regulate the voltage and reduce the power curtailment while serving requisite load demand. Both RPC and APC are integrated to improve the voltage regulation margin in a low-voltage distribution network. Using bus voltage sensitivities, the integrated RPC and APC are performed on the most sensitive PV inverter and BESS for voltage regulation. The proposed PCA has a large capability in voltage regulation as it performs RPC before APC, reducing the amount of total PV power curtailment. Simulation studies are conducted on distribution feeders to demonstrate the effectiveness of the proposed approach.

关键词： Voltage control Inverters Reactive power Sensitivity Distribution networks Photovoltaic systems algorithm design and analysis Coordinated power compensation algorithm overvoltage power curtailment voltage sensitivities

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Adaptive Quantized Estimation Fusion Using Strong Tracking Filtering and Variational Bayesian

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第3期50卷 899-910页

作者： Ge, Quanbo Wei, Zhongliang Liu, Mingxin Yu, Junzhi Wen, Chenglin Hangzhou Dianzi Univ Sch Automat Inst Syst Sci & Control Engn Hangzhou 310018MMM Peoples R China Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 232001 Peoples R China Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Chinese Acad Sci Inst Automat State Key Lab Complex Syst Management & Control Beijing 100190 Peoples R China

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications.

关键词： Estimation Quantization (signal) Kalman filters Bayes methods algorithm design and analysis Real-time systems Adaptation models Decentralized fusion multiple method fusion quantization strong tracking filter variational Bayesian (VB)

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Min-Max Tours and Paths for Task Allocation to Heterogeneous Agents

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第3期7卷 1511-1522页

作者： Prasad, Amritha Choi, Han-Lim Sundaram, Shreyas Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 300100 South Korea

We consider a scenario consisting of a set of heterogeneous mobile agents and a set of tasks dispersed over a geographic area. The agents are partitioned into different types. The tasks are partitioned into specialized tasks that can only be done by agents of a certain type, and generic tasks that can be done by any agent. Given this scenario, we address the problem of allocating these tasks among the available agents (subject to type compatibility constraints) while minimizing the maximum travel cost for any agent. We first look at the heterogeneous agent cycle problem where agents start at a common depot and need to tour the set of tasks allocated to them before returning to the depot. We provide a 5-approximation algorithm to solve this problem, regardless of the total number of agents and the number of agents of each type. We then consider the heterogeneous agent path problem (HAPP) where agents can start from arbitrary locations and are not constrained to return to their start location. We consider two approaches to solve HAPP. The first approach yields a 15-approximation factor, while the second yields a factor of 16.

关键词： algorithm design and analysis approximation algorithms multirobot systems optimal scheduling shortest path problems

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Adapted Compressed Sensing: A Game Worth Playing

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IEEE CIRCUITS AND SYSTEMS MAGAZINE 2020年第1期20卷 40-60页

作者： Mangia, Mauro Pareschi, Fabio Rovatti, Riccardo Setti, Gianluca Univ Bologna Stat Signal Proc Grp ARCES I-40126 Bologna Italy Univ Bologna ARCES I-40126 Bologna Italy Politecn Torino Dept Elect & Telecommun Turin Italy Univ Bologna I-40126 Bologna Italy Politecn Torino Turin Italy

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing.

关键词： Compressed sensing Complexity theory Encoding algorithm design and analysis

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analysis of Unbounded Petri Net With Lean Reachability Trees

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第6期50卷 2007-2016页

作者： Li, Jun Yu, Xiaolong Zhou, MengChu Southeast Univ Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia

At present, no efficient method is proposed for the liveness analysis of general unbounded Petri nets (UPNs) except some of their subclasses. Our previous work presents a non-Karp-Miller finite reachability tree, i.e., lean reachability tree (LRT) to represent their markings. It faithfully expresses and folds the reachability set of an unbounded net. It can totally avoid the efforts made by the existing modified Karp-Miller trees on the expression of potentially unbounded nodes and elimination of all fake markings. By exploiting it, this paper presents a method for comprehensively analyzing the properties of general UPNs. Particularly, we reveal the repeatability of deadlock with the unfolding of some unbounded leaves in LRT and present a sufficient and necessary condition of deadlock existence. Then, LRT and some partial trees generated from it, instead of entire reachability graphs, are utilized to analyze the liveness and reversibility of general UPNs rather than some special ones. The related theoretical results are proven. A unified algorithm based on LRT for analysis of boundedness, liveness, deadlock, and reversibility of general UPNs is developed for the first time. The results of a case study show that the presented method is effective for general UPNs.

关键词： Light rail systems System recovery Petri nets algorithm design and analysis Cybernetics Indexes Tools Boundedness deadlock discrete event systems liveness Petri nets (PNs) reachability tree reversibility unbounded Petri nets

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Energy-Efficient IoT Sensor Calibration With Deep Reinforcement Learning

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IEEE ACCESS 2020年 8卷 97045-97055页

作者： Ashiquzzaman, Akm Lee, Hyunmin Um, Tai-Won Kim, Jinsul Chonnam Natl Univ Sch Elect & Comp Engn Gwangju 59626 South Korea Korea Elect Technol Inst Human IT Convergence Res Ctr Seoul 02792 South Korea Duksung Womens Univ Dept Cyber Secur Coll Sci & Technol Seoul 132714 South Korea

The modern development of ultra-durable and energy-efficient IoT based communication sensors has much application in modern telecommunication and networking sectors. Sensor calibration to reduce power usage is beneficial to minimizing energy consumption in sensors as well as improve the efficiency of devices. Reinforcement learning (RL) has been received much attention from researchers and now widely applied in many study fields to achieve intelligent automation. Though various types of sensors have been widely used in the field of IoT, rare researches were conducted in resource optimizing. In this novel research, a new style of power conservation has been explored with the help of RL to make a new generation of IoT devices with calibrated power sources to maximize resource utilization. A closed grid multiple power source based control for sensor resource utilization has been introduced. Our proposed model using Deep Q learning (DQN) enables IoT sensors to maximize its resource utilization. This research focuses solely on the energy-efficient sensor calibration and simulation results show promising performance of the proposed method.

关键词： algorithm design and analysis optimization computational and artificial intelligence battery management systems simulation electronic design automation and methodology deeplearning reinforcement learning

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A Knowledge-Based Cooperative algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第5期50卷 1805-1819页

作者： Wang, Jing-Jing Wang, Ling Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Facing increasingly serious ecological problems, sustainable development and green manufacturing have attracted much attention. Meanwhile, with the development of globalization, distributed manufacturing is becoming widespread. This paper addresses an energy-efficient scheduling of the distributed permutation flow-shop (EEDPFSP) with the criteria of minimizing both makespan and total energy consumption. Considering the distributed and multiobjective optimization complexity, a knowledge-based cooperative algorithm (KCA) is proposed to solve the EEDPFSP. First, a cooperative initialization scheme is presented with both extended energy-efficient Nawaz-Enscore-Ham heuristic and slowest allowable speed rule that are specially designed to produce good initial solutions with certain diversity. Second, several properties of the nondominated solutions are investigated based on the characteristics of the bi-objective problem, which are used to develop the knowledge-based search operators. Third, a cooperative search strategy of multiple operators is designed for the solutions with different characteristics to tradeoff two objectives. Fourth, a knowledge-based local intensification is used for exploiting better nondominated solutions sufficiently. Moreover, an energy saving method based on the critical path is used to further improve the performance. The effect of parameter setting on the KCA is investigated with the Taguchi method of design-of-experiment. Extensive computational tests and comparisons are carried out, which verify the effectiveness of the special designs of the KCA in solving the EEDPFSP.

关键词： Production facilities Job shop scheduling algorithm design and analysis Knowledge based systems Optimization Carbon dioxide Green products Cooperation distributed permutation flow-shop scheduling energy efficient knowledge makespan total energy consumption (TEC)

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