Augmented Lagrangian coordination for energy-optimal allocation of smart manufacturing services

The rapid development of information and communication technologies has triggered the proposition and implementation of smart manufacturing paradigms. In this regard, efficient allocation of smart manufacturing services (SMSs) can provide a sustainable manner for promoting cleaner production. Currently, centralized optimization methods have been widely used to complete the optimal allocation of SMSs. However, personalized manufacturing tasks usually belong to diverse production domains. The centralized optimization methods could hardly include related production knowledge of all manufacturing tasks in an individual decision model. Consequently, it is difficult to provide satisfactory SMSs for meeting customer's requirements. In addition, energy consumption is rarely considered in the SMS allocation process which is unfavorable for performing sustainable manufacturing. To address these challenges, augmented Lagrangian coordination (ALC), a novel distributed optimization method is proposed to deal with the energy-optimal SMS allocation problem in this paper. The energy-optimal SMS allocation model is constructed and decomposed into several loose-coupled and distributed elements. Two variants of the ALC method are implemented to formulate the proposed problem and obtain final SMS allocation results. A case study is employed to verify the superiority of the proposed method in dealing with energy-optimal SMS allocation problems by comparing with the centralized optimization method at last.     

Automatic segmentation and summarization for videos taken with smart glasses

Multimedia Tools and Applications - This paper discusses the topic of automatic segmentation and extraction of important segments of videos taken with Google Glasses. Using the information from...     

基于机器视觉的智能导盲眼镜设计

提出一种基于机器视觉的智能导盲眼镜系统的设计方案。采用三星公司Cortex-A8架构的S5PV210作为中央处理器,搭载Linux系统,配备双目采集、GPS定位、语音播报、GSM短信、语音通话、无线传输六大核心功能模块搭建智能导盲眼镜系统的硬件平台,结合深度学习算法在远程云服务器上完成了对目标场景的智能识别,最后以语音的形式实时对盲人的行走作出准确引导。系统测试结果表明,该智能导盲眼镜系统在测试环境下不仅能对盲人出行正确导航,还具有一定的目标识别能力,能帮助盲人进行简易物品归类。该系统还兼有GPS定位、语音通话、GSM短信等多项辅助功能。     

You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data.     

Augmented reality assisted learning achievement,motivation, and creativity for children of low-grade in primary school

There is a large volume of published studies describing the positive role of Augmented Reality (AR) in educational settings. However, there is a paucity of empirical work that explores its role in supporting primary school pupil's motivation and creativity. Moreover, numerous studies have confirmed that pupils have difficulties in learning geometry and thinking geometrically. In this context, this paper explores the possibility of using AR software for low-grade primary school pupils to enhance their creative thinking and increase their motivation during the informal geometry training course. A research-based design process is adopted to replicate the primary school pupil's learning in this study, where pupils in the experimental group used mobile application to access AR virtual manipulatives, pupils in the control group used physical manipulatives. The applied AR technology allows pupils to handle 2D and 3D rounded corners and simple closed curves to find the most effective solutions for some learning challenges. Sixty-two pupils from the first-grade primary school in Egypt have participated in this study with the aim of investigating: (a) the primary school pupil's achievements and creative thinking in using AR techniques, and (b) the specific strategies that can increase their motivation and engagement. The most obvious finding to emerge from this study is that there was no statistically significant difference between the groups in geometry learning achievement, while statistically significant differences were found between the groups in the motivation and creative thinking skills in favour of the experimental group.     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

     

Augmented interaction with physical books in an Ambient Intelligence learning environment

This paper presents an augmented reality environment for students' improved learning, which is based on unobtrusive monitoring of the natural reading and writing process. This environment, named SESIL, is able to perform recognition of book pages and of specific elements of interest within a page, as well as to perceive interaction with actual books and pens/pencils, without requiring any special interaction device. As a result, unobtrusive, context - aware student assistance can be provided. In this way, the learning process can be enhanced during reading with the retrieval and presentation of related material and, during writing, by the provision of assistance to accomplish writing tasks whenever appropriate. The SESIL environment is evaluated in terms of robustness, accuracy and usability.     

Design of affectively evocative smart ambient media for learning

This study proposes a teaching and research initiative, named SAMAL (Smart AMbience for Affective Learning) that will provide a unique ambient mediated environment for integrating cognitive and affective approaches to enhance learning. Also, this study illustrates a design of SAMAL classroom with affectively evocative scenarios for learning de Bono’s six-hat thinking in a course of information management, and proposes a research model to examine the enabling effects of the SAMAL environment on learners’ affective experience, learning engagement and creativity. Our empirical results show that SAMAL environment can positively influence the affective experience perceived by learners, encourage their learning engagement, which in turn positively influence their creativity. In particular, the results show that the effect of SAMAL environment on students’ learning engagement was mediated by the affective experience they perceived.     

设备端基于深度学习的智能家居服务推荐框架

随着智能家居的普及,用户期望通过自然语言指令实现智能设备的控制,并希望获得个性化的智能家居服务。然而,现有的挑战包括智能设备的互操作性和对用户环境的全面理解。针对上述问题,提出一个支持设备端用户智能家居服务推荐个性化的框架。首先,构建智能家居的运行时知识图谱,用于反映特定智能家居中的上下文信息,并生成用例场景语句;其次,利用预先收集的通用场景下,用户的自然语言指令和对应的用例场景语句训练出通用推荐模型;最后,用户在设备端以自然语言管理智能家居设备和服务,并通过反馈微调通用模型的权重得到个人模型。在基本指令集、复述集、场景指令集三个数据集上的实验表明,用户的个人模型相比于词嵌入方法的准确率提升了6.5%~30%,与Sentence-BERT模型相比准确率提升了2.4%~25%,验证了设备端基于深度学习的智能家居服务框架具有较高的服务推荐准确率,能够有效地管理智能家居设备和服务。     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

Augmented reality technologies,systems and applications

This paper surveys the current state-of-the-art of technology, systems and applications in Augmented Reality. It describes work performed by many different research groups, the purpose behind each new Augmented Reality system, and the difficulties and problems encountered when building some Augmented Reality applications. It surveys mobile augmented reality systems challenges and requirements for successful mobile systems. This paper summarizes the current applications of Augmented Reality and speculates on future applications and where current research will lead Augmented Reality’s development. Challenges augmented reality is facing in each of these applications to go from the laboratories to the industry, as well as the future challenges we can forecast are also discussed in this paper. Section 1 gives an introduction to what Augmented Reality is and the motivations for developing this technology. Section 2 discusses Augmented Reality Technologies with computer vision methods, AR devices, interfaces and systems, and visualization tools. The mobile and wireless systems for Augmented Reality are discussed in Section 3. Four classes of current applications that have been explored are described in Section 4. These applications were chosen as they are the most famous type of applications encountered when researching AR apps. The future of augmented reality and the challenges they will be facing are discussed in Section 5.     

An ambient intelligence approach for learning in smart robotic environments

Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.     

Design and implementation of deep learning strategy based smart signature verification System

The signature verification is broadly used for personal identification. The person is identified automatically using signature verification method to avoid forgery persons. The signature verification is classified into the static method and the dynamic method. The static verification method is based on stored images and the dynamic verification method is based on dynamic features of the signature. The integer wavelet transformation method is used to identify the breath and height ratio of the signature features. In addition to that spurious noise also removed before extracting the signature feature. And the signature is isolated from the background of the images. The extracted feature is analyzed using integer wavelet transformation and a neural network is selected to decide according to that original and forgery signature. As compared with the conventional system the proposed found to be about 20% error ratio. The database SVC2004 is selected to verify the signature.     

A comfort analysis of using smart glasses during "picking" and "putting" tasks

Logistics and operations managers are always evaluating ways to improve their order picking operations. While there are numerous picking studies, currently there is a void in research focused on improving productivity and efficiency for putting-type warehousing tasks such as receiving, storing, and shipping. Studies have shown that the logistics industry demonstrates significant improvement when using head mounted display solutions for order picking tasks. However, little research has been completed on the comfort of head mounted displays such as smart glasses and how to alter them to increase their product lifecycle. Altered Vuxiz® glasses are used in this work to study comfort, preference, and performance while executing common logistical order picking and shipment putting tasks. The impact of design type (weighted front, side, or back) was investigated using comfort rating scales. Device preference surveys, the participants adjustments made to the glasses and task performance were also captured. There was no significant difference in device preference regardless of task type. Despite the side weighted arrangement being the most comfortable, the participants still were uncomfortable. For companies considering investing in smart glasses, other alternatives should be considered to extend the lifecycle of head mounted display devices.     

Learning how, what, and whether to communicate: emergence of protocommunication in reinforcement learning agents

This paper examines whether and how a primitive form of communication emerges between adaptive agents by using their excess degrees of freedom in action and perception. As a case study, we consider a game in which two reinforcement learning agents learn to earn rewards by intruding into the other’s territory. Our simulation shows that agents with lights and light sensors can learn turn-taking behavior for avoiding collisions using visual communication. Further analysis reveals a variety in the mapping of messages to signals. In some cases, the differentiation of roles into a sender and a receiver was observed. The result confirmed that protocommunication can emerge through interaction between agents having generic reinforcement learning capability. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Big data and smart cities: a public sector organizational learning perspective

Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning.     

Incremental and accurate computation of machine learning models with smart data summarization

Journal of Intelligent Information Systems - Nowadays, data scientists prefer “easy” high-level languages like R and Python, which accomplish complex mathematical tasks with a few lines...     

Augmented reality and competition in robotics education: Effects on 21st century competencies,group collaboration and learning motivation

Robotics education has received an increasing attention in recent years as a means to build students' motivation, team collaboration skills, and other valuable 21st century competencies. Yet there is a lack of experimental studies to investigate and identify strategies to facilitate robotics education. This study adopted a 2 × 2 quasi-experimental design to investigate two strategies: the incorporation of augmented reality (AR) and the introduction of competition in robotics activities. Students' robotics task performance, team collaboration processes, 21st century learning competencies and learning motivation were measured as dependent variables. The results indicated that AR significantly improved students' motivation, team processes, and 21st century competencies. Moreover, the effects of AR were more pronounced with the competition groups. Implications are drawn to provide guidelines on the use of AR and competition in robotics education.     

Interactivity, interfaces, and smart toys

Although computers can represent a medium for children's social and intellectual development, some researchers believe that using computers before age seven subtracts from important developmental tasks and other types of learning. Those opposed to computers believe that computer-based activities are less effective in developing understanding and skills than are artifacts that young children can handle. These anxieties extend to technologies such as smart toys. Our recently completed research project, Computers and Children's Electronic Toys, investigated how children use smart toys. Cachet combines recent interest in mobile learning, tangible interfaces, and the home use of technologies. This research aimed mainly to explore interactivity and interfaces in the context of smart toys that children could use alone or in conjunction with a computer.