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IEEE Potentials 2009年第6期28卷 19-24页

作者： Stroble, Jacquelyn K. Watkins, Steve Stone, Robert American Institute of Physics Discoveries Breakthroughs Inside Science Program United States Department of Electrical and Computer Engineering Applied Optics Laboratory Missouri University of Science and Technology United States Mechanical Industrial and Manufacturing Engineering Department Oregon State University United States

Biology has played a key role in influencing the development and optimization of a significant variety of sensors that provide engineers new ideas for improvements to current technology, new sensor technology, and potential sensor miniaturization. Biological systems, such as plants and insects offer significant methods of flight, imaging, sensing, adaptation to environment, and locomotion. The biological domain provides inspiration at many levels, such as cellular, organism, and species. A biological system is any biological organism, organism subsystem, or portion of an organism that is observed to exist. Abstraction of the biological system using analogical reasoning allows an engineering designer to look for a specific solution through mimicry in an approach to biology-inspired design. The analogous approach relies on the idea of figuring out the basis of overall biological system and of each part or subsystem that achieves the overall goal.

关键词： Biological systems

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Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Aggregated Problem Information 21

Protein Structure Prediction Using Dynamic Speciation Evolut...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Parpinelli, Rafael Stubs Wojeicchowski, Nicholas Will, Nilcimar Neitzel State University of Santa Catarina Graduate Program in Applied Computing Joinville Brazil State University of Santa Catarina Computer Science Department Joinville Brazil

ISBN: (纸本)9798350356632

Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time. © 2024 IEEE.

关键词： Prediction models

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Learning negotiation policies using IB3 and bayesian networks

Learning negotiation policies using IB3 and bayesian network...

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11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010

作者： Nalepa, Gislaine M. Ávila, Bráulio C. Enembreck, Fabrício Scalabrin, Edson E. Graduate Program on Applied Computer Science PUCPR Pontifical Catholic University of Paraná R. Imaculada Conceição Curitiba PR 1155 Brazil

ISBN: (纸本)3642153801

This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Bayesian networks

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Theoretical Modelling of Smart Meter Privacy Protection with Multi-meter Energy Routing 4

Theoretical Modelling of Smart Meter Privacy Protection with...

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4th IEEE International Conference on Smart Internet of Things, SmartIoT 2020

作者： Ergen, Cankut Gulbahar, Burhan Ozyegin University Graduate School of Engineering and Science MSc Program of Computer Science Istanbul34794 Turkey Ozyegin University Applied Research Center of Technology Products Istanbul34794 Turkey

ISBN: (纸本)9781728165141

Potential of intrusion during smart meter data collection is an important problem for household privacy in next-generation smart homes. There are various privacy protection methods such as hiding the real usage with renewable batteries (RBs) and encrypted communication. There are privacy threats due to the detection of the energy transfer through RBs while encryption-based methods result in high computational complexity. In this study, a novel privacy improvement mechanism is proposed based on the routing of energy through multiple smart meters among multiple houses in a neighborhood. The optimization of privacy based on uniform randomization is theoretically modeled and some heuristic solutions are presented. In this method, energy transfer is randomly distributed among multiple smart meters, real usage of a house is buffered with the RB and smart meter indices are randomly selected. In numerical simulations, privacy improvement is shown by utilizing the random selection of smart meter indices for randomizing the energy routing and the buffers of RBs. Furthermore, energy privacy (EP) metric validating the simulation results is defined. Energy routing with multiple smart meters provides a practical and low complexity privacy protection mechanism for next-generation smart grid architectures. © 2020 IEEE.

关键词： Smart meters

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A Shoe Shopping System based on Convolutional Neural Network Image Recognition 5

A Shoe Shopping System based on Convolutional Neural Network...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Chen, Chen-Sheng Chen, Jian-An Yeh, Shih-Hsien Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Program in Engineering Science Hsinchu Taiwan Chang Gung University Department of Computer Science and Information Engineering Taoyuan Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system

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EAVIRA: Energy-Aware Virtual Infrastructure Reallocation Algorithm

EAVIRA: Energy-Aware Virtual Infrastructure Reallocation Alg...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Denivy B. Ruck Maurício A. Pillon Charles C. Miers Guilherme P. Koslovski Department of Computer Science Graduate Program in Applied Computing

The elastic provisioning of Virtual Infrastructures (VIs) enables a dynamic management of cloud resources (computing and communication) in order to meet the hosted application's requirements. Thus, to perform elasticity requests, providers usually rely on reallocation mechanisms and policies. The concerns regarding the environment and the operational costs indicate energy consumption of the data centers as recurring topic in providers policies. Moreover, energy-aware provisioning is beneficial for tenants also. Recent cost models have introduced an implicit incentive to use computing and networking resources just when need to avoid high rent costs. However, reallocation and elasticity requests can unbalance the Data Center (DC) unnecessarily increasing the number of active servers. In this paper we propose EAVIRA algorithm, which takes into account the proportional sharing of CPU usage of DC servers to calculate individual usage costs to: disable idle equipments, and reallocate VIs. EAVIRA acts on online requests for elasticity configuration and performs an offline load balancing, triggered by the Infrastructure as a Service (IaaS) provider. Our experimental analysis indicates a reduction of energy consumption and an increasing on acceptance ratio of allocation requests.

关键词： Resource management Servers Energy consumption Elasticity Cloud computing Load management Computational modeling

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Applying Verbal Decision Analysis in Distributed Software Development: Rank Ordering the Influencing Factors in Task Allocation

Applying Verbal Decision Analysis in Distributed Software De...

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Iberian Conference on Information Systems and Technologies

作者： Marum Simao Filho Placido Rogerio Pinheiro Adriano Bessa Albuquerque Graduate Program in Applied Computer Science University of Fortaleza

ISBN: (纸本)9781509012268

In the management of distributed software development, project managers face many challenges. Some of the most critical ones are related to the allocation of tasks between remote teams. To assign a task to a site or team, the project manager should take into account several factors such as team's technical expertise and cultural similarities. He or she usually makes this decision in a subjective way. The verbal decision analysis is an approach based on solving problems through multi-criteria qualitative analysis, which means it considers the analysis of subjective criteria. This paper describes the application of verbal decision analysis method ZAPROS III-i to rank order the factors that the project managers should take into account when allocating tasks in distributed software development projects.

关键词： Distributed Software Development Task Allocation Verbal Decision Analysis ZAPROS III-i

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Truck driver scheduling and Australian heavy vehicle driver fatigue law

Truck driver scheduling and Australian heavy vehicle driver ...

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8th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2010

作者： Goel, Asvin MIT Zaragoza International Logistics Program Zaragoza Logistics Center Spain Department of Computer Science Applied Telematics/e-Business Group University of Leipzig Germany

In September 2008 new regulations for managing heavy vehicle driver fatigue entered into force in Australia. According to the new regulations there is a chain of responsibility ranging from drivers to dispatchers and shippers. Thus, carriers must explicitly consider driving and working hour regulations when generating truck driver schedules. This paper presents various heuristics for scheduling driving and working hours of Australian truck drivers.

关键词： Truck drivers

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Detecting drifts in multi-issue negotiations

Detecting drifts in multi-issue negotiations

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12th Ibero-American Conference on Advances in Artificial Intelligence, AI 2010

作者： Nalepa, Gislaine M. Ávila, Bráulio C. Enembreck, Fabrício Scalabrin, Edson E. PUCPR Pontifical Catholic University of Paraná PPGIA Graduate Program on Applied Computer Science R. Imaculada Conceição 1155 Curitiba PR Brazil

ISBN: (纸本)3642169511

In an automated negotiation process, it is usual for one party not to reveal their preferences to its opponent. In an attempt to adjust itself to this process, each trade agent can be endowed with capabilities of learning and detecting its opponent's changing preferences. This paper presents techniques for drift detection that are useful in this scenario: the Instance-Based Learning algorithms (IB1, IB2 and IB3) and the Bayesian Networks. Theoretically, a group of expert agents is able to achieve better results than an individual, therefore, the DWM is part of these studies, once it provides an effective strategy for integrating the decision of several expert agents. The experiments performed revolve around the settings DWM-IB3 and DWM-Bayesian Network. The performance of each system was evaluated for gradual, moderate and abrubt drifts in concept, and the results showed that both settings are able to efficiently detect drifts in the preferences of the opponent. © 2010 Springer-Verlag.

关键词： Bayesian networks

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The Influence of Workplace Environment on Employee Turnover at Delmon Poultry Company: Key Insights and Findings

The Influence of Workplace Environment on Employee Turnover ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Alaghbari, Mohammed Abdulrazzaq Ateeq, Ali Milhem, Marwan Garad, Askar Ahmed Yusuf, Ammar Yusuf College of Administrative Sciences Department of Business Administration Applied Science University Bahrain Bahrain College of Administrative and Financial Science Administrative Science Department Gulf University Sanad26489 Bahrain Universitas Muhammadyiah Yogyakarta Magister Program Yogyakarta Indonesia College of Arts and Science Department of Computer Science Applied Science University Bahrain Bahrain

ISBN: (纸本)9798350369106

The present study examines how organizational work environments affect employee turnover at Delmon Poultry Company. The researcher used a structured questionnaire and a quantitative approach to collect data from 340 employees. The results show that the organizational work environment has a greater impact on employee attrition than the social environment, accounting for 75.5% of the turnover variance. Although the social environment is substantial, it only accounts for 29.2% of the variance. The research establishes that attrition reduction necessitates the enhancement of organizational factors, including policy impartiality and management practices. In order to offer a more comprehensive understanding of attrition dynamics, future research should investigate these relationships across various industries and over time. The study contributes to the literature by emphasizing the significance of a supportive organizational environment for employee retention and providing practical recommendations for human resource management to improve employee retention strategies. © 2024 IEEE.

关键词： Employee Turnover Human Resource Management Organizational Environment Workplace Environment

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