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A Micro-Grid Distributed Intelligent Control and Management System

IEEE TRANSACTIONS ON SMART GRID 2015年第6期6卷 2964-2974页

作者： Kohn, Wolf Zabinsky, Zelda B. Nerode, Anil Atigeo LLC Bellevue WA 98004 USA Univ Washington Dept Ind & Syst Engn Seattle WA 98195 USA Cornell Univ Ithaca NY 14853 USA

Micro-grids require active control to maintain quality of service and to interface with the power grid in a bi-directional manner. Further, micro-grids must be justified by environmental, governmental, and economic viability. We present a programmable architecture for active, optimal distributed control of elements of the grid to achieve desired behavior. A unique aspect of this architecture is to include a distributed inductive engine for learning the local dynamics of generators and loads in the micro-grid. It generates feedback laws that are adapted to the current status of the micro-grid, and responds to anomalous events in a resilient manner. An important novelty is that control laws are extracted online for bidirectional discontinuous nonlinear loads by mean field methods from physics outside the standard design methodologies for piecewise linear quadratic controls.

关键词： Bidirectional power flow computational and artificial intelligence decentralized control distributed parameter systems learning systems micro-grids power engineering and energy power system control power system management smart grids

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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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Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第5期36卷 2069-2081页

作者： Sun, Peijie Wu, Le Zhang, Kun Chen, Xiangzhi Wang, Meng Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Anhui Peoples R China Hefei Univ Technol Intelligent Interconnected Syst Lab Anhui Prov Hefei 230009 Anhui Peoples R China

While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to introduce additional self-supervised signals to address this. However, this approach often unintentionally distances the target user/item from their collaborative neighbors, limiting its efficacy. In response, we propose a solution that treats the collaborative neighbors of the anchor node as positive samples within the final objective loss function. This paper focuses on developing two unique supervised contrastive loss functions that effectively combine supervision signals with contrastive loss. We analyze our proposed loss functions through the gradient lens, demonstrating that different positive samples simultaneously influence updating the anchor node's embeddings. These samples' impact depends on their similarities to the anchor node and the negative samples. Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model. Our proposed Neighborhood-Enhanced Supervised Contrastive Loss (NESCL) model substitutes the contrastive loss function in SGL with our novel loss function, showing marked performance improvement. On three real-world datasets, Yelp2018, Gowalla, and Amazon-Book, our model surpasses the original SGL by 10.09%, 7.09%, and 35.36% on NDCG@20, respectively.

关键词： Data models Task analysis Collaboration Collaborative filtering Training computational modeling Self-supervised learning artificial intelligence computers and information processing computational and artificial intelligence information retrieval information filtering learning systems nearest neighbor methods professional communication pattern recognition recommender systems self-supervised learning

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An Enhanced Solver for the Game of Amazons

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IEEE TRANSACTIONS ON computational intelligence AND AI IN GAMES 2015年第1期7卷 16-27页

作者： Song, Jiaxing Mueller, Martin Univ Alberta Dept Comp Sci Edmonton AB T6G 2E3 Canada

The game of Amazons is a modern board game with simple rules and nice mathematical properties. It has a high computational complexity. In 2001, the starting position on a 5 5 board was proven to be a first player win. The enhanced Amazons solver presented here extends previous work in the following five ways: by building more powerful endgame databases, including a new type of databases for so-called blocker territories, by improving the rules for computing bounds on complex game positions, by local search to find tighter local bounds, by using ideas from combinatorial game theory to find wins earlier, and by using a df-pn based solver. Using the improved solver, the starting positions for Amazons on the 4 x 5, 5 x 4, 4 x 6, 5 x 6, and 4 x 7 boards were shown to be first player wins, while 6 x 4 is a second player win. The largest proof, for the 5 x 6 board, is presented in detail.

关键词： artificial intelligence computational and artificial intelligence combinatorial mathematics heuristic algorithms search methods search problems algorithms

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Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2019年第8期38卷 1971-1980页

作者： Man, Yunze Huang, Yangsibo Feng, Junyi Li, Xi Wu, Fei Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China

The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN-based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas. Furthermore, deformable U-Net captures geometry aware information of pancreas by learning geometrically deformable filters for feature extraction. The experiments on NIH dataset validate the effectiveness of the proposed framework in pancreas segmentation.

关键词： computational and artificial intelligence image processing image segmentation imaging tomography computed tomography pancreas segmentation DQN deformable U-net

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Evolving intelligent agents: A 50 year quest

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IEEE computational intelligence MAGAZINE 2008年第1期3卷 12-17页

作者： De Jong, Kenneth A. George Mason Univ Fairfax VA 22030 USA

Already in the early 1960s, Larry Fogel and his colleagues were exploring the possibility of creating artificial intelligence using simulated evolution. Over the past 50 years, that idea has captured the imagination of many people and has led to a wide variety of approaches. In this article, this quest is summarized, the current state of the art is described, and some of the remaining open questions are discussed.

关键词： Intelligent agent Machine intelligence Automata artificial intelligence computational and artificial intelligence Programming Machine learning Competitive intelligence History Computer languages

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MODELS VERSUS RULES, DEEP VERSUS COMPILED, CONTENT VERSUS FORM - SOME DISTINCTIONS IN KNOWLEDGE SYSTEMS RESEARCH

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IEEE EXPERT-INTELLIGENT SYSTEMS & THEIR APPLICATIONS 1991年第2期6卷 75-79页

作者： CHANDRASEKARAN, B Ohio State Uinversity USA

Four distinctions that are made in the literature on knowledge system design are examined. They are: model-based versus rule-based problem solving, models versus experiential or associational knowledge, deep versus compiled knowledge, and content versus form. The focus is on the diagnostic task, since these distinctions have most often been debated in the context of this problem, but the issues are relevant to knowledge systems in general.< >

关键词： Knowledge based systems Computer languages Humans Valves Problem-solving Computer architecture Vocabulary computational and artificial intelligence Intelligent structures computational modeling

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Efficient Detection of Botnet Traffic by Features Selection and Decision Trees

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IEEE ACCESS 2021年 9卷 120567-120579页

作者： Velasco-Mata, Javier Gonzalez-Castro, Victor Fernandez, Eduardo Fidalgo Alegre, Enrique Univ Leon Dept Elect Syst & Automat Engn Leon 24071 Spain Spanish Natl Cybersecur Inst INCIBE Leon 24005 Spain

Botnets are one of the online threats with the most significant presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze extensive network traffic data. In this work, we focus on increasing the performance of botnet traffic classification by selecting those features that further increase the detection rate. For this purpose, we use two feature selection techniques, i.e., Information Gain and Gini Importance, which led to three pre-selected subsets of five, six and seven features. Then, we evaluate the three feature subsets and three models, i.e., Decision Tree, Random Forest and k-Nearest Neighbors. To test the performance of the three feature vectors and the three models, we generate two datasets based on the CTU-13 dataset, namely QB-CTU13 and EQB-CTU13. Finally, we measure the performance as the macro averaged F1 score over the computational time required to classify a sample. The results show that the highest performance is achieved by Decision Trees using a five feature set, which obtained a mean F1 score of 85% classifying each sample in an average time of 0.78 microseconds.

关键词： Botnet Feature extraction Malware Detectors Radio frequency Decision trees Support vector machines computational and artificial intelligence computer applications decision trees machine learning algorithms optimization methods

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Procedural Content Generation via Machine Learning (PCGML)

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IEEE TRANSACTIONS ON GAMES 2018年第3期10卷 257-270页

作者： Summerville, Adam Snodgrass, Sam Guzdial, Matthew Holmgard, Christoffer Hoover, Amy K. Isaksen, Aaron Nealen, Andy Togelius, Julian Univ Calif Santa Cruz Dept Computat Media Santa Cruz CA 95064 USA Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Duck & Cover Games ApS DK-1311 Copenhagen Denmark Northeastern Univ Coll Arts Media & Design Boston MA 02215 USA NYU Dept Comp Sci & Engn Brooklyn NY 11201 USA

This survey explores procedural content generation via machine learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement;this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content, such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content, such as sprites and sound effects. In addition to using PCG for autonomous generation, cocreativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content. Multiple PCGML methods are covered, including neural networks: long short-term memory networks, autoencoders, and deep convolutional networks;Markov models: n-grams and multidimensional Markov chains;clustering;and matrix factorization. Finally, we discuss open problems in PCGML, including learning from small data sets, lack of training data, multilayered learning, style-transfer, parameter tuning, and PCG as a game mechanic.

关键词： computational and artificial intelligence design tools electronic design methodology knowledge representation machine learning pattern analysis procedural content generation (PCG)

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eDKM: An Efficient and Accurate Train-Time Weight Clustering for Large Language Models

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2024年第1期23卷 37-40页

作者： Cho, Minsik Vahid, Keivan A. Fu, Qichen Adya, Saurabh Mundo, Carlo C. Del Rastegari, Mohammad Naik, Devang Zatloukal, Peter Apple Cupertino CA 95014 USA

Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices for faster responses and better privacy protection. However, the size of LLMs (i.e., billions of parameters) requires highly effective compression to fit into storage-limited devices. Among many compression techniques, weight-clustering, a form of non-linear quantization, is one of the leading candidates for LLM compression, and supported by modern smartphones. Yet, its training overhead is prohibitively significant for LLM fine-tuning. Especially, Differentiable KMeans Clustering, or DKM, has shown the state-of-the-art trade-off between compression ratio and accuracy regression, but its large memory complexity makes it nearly impossible to apply to train-time LLM compression. In this letter, we propose a memory-efficient DKM implementation, eDKM powered by novel techniques to reduce the memory footprint of DKM by orders of magnitudes. For a given tensor to be saved on CPU for the backward pass of DKM, we compressed the tensor by applying uniquification and sharding after checking if there is no duplicated tensor previously copied to CPU. Our experimental results demonstrate that eDKM can fine-tune and compress a pretrained LLaMA 7B model from 12.6 GB to 2.5 GB (3 b/weight) with the Alpaca dataset by reducing the train-time memory footprint of a decoder layer by 130x, while delivering good accuracy on broader LLM benchmarks (i.e., 77.7% for PIQA, 66.1% for Winograde, and so on).

关键词： Tensors Memory Sharding Optimization Indexes Graphics processing units Complexity theory computational and artificial intelligence artificial intelligence learning systems machine learning deep learning

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