RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Innovation in the software industry: two case studies

This paper examines how two small companies adopted new business strategies to exploit the market opportunities for web-enabled software. The first case study (Beaumont Travel) transformed itself from a bus company into a software house for the transportation industry, producing software modules for central daily business management functions and field-bus passenger and vehicle maintenance information. In the second case study, a small software house (QEB Solutions) saw the opportunity to develop web portals to existing back-end systems, and has worked with a range of customers to develop a new revenue stream that will support the company in future years. Both these business projects were undertaken via the Knowledge Transfer Partnership (KTP) scheme, which supports university academics working with industry on strategic projects

Quantum Dynamic Optimization Algorithm for Neural Architecture Search on Image Classification

Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks

Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT

Federated Learning (FL) allows data owners to train neural networks together without sharing local data, allowing the Industrial Internet of Things (IIoT) to share a variety of data. However, traditional federated learning frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposed a dual-blockchain based multi-layer grouping federated learning architecture (BMFL). BMFL divides the participant groups based on the training tasks, then realizes the model training combining synchronous and asynchronous through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault dataset. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable

Modeling and Experimental Study of a Novel Multi-DOF Parallel Soft Robot

In view of the demand for flexible drive and large load of the soft robot in the practical application, a novel type of flexible-actuated multi-degree-of-freedom (multi-DOF) parallel soft robot is designed. The proposed robot in parallel combination of three groups of flexible-actuated elements (FAEs) realizes large load by increasing the bearing area at the connection between flexible-actuated units (FAUs). In order to improve the driving flexibility, the multi-layer FAU is used to drive independently in parallel so as to realize omnidirectional bending movement by pneumatic drive. With the coupled analysis in terms of motion and force, the mapping model of kinematic attitude parameters and the external load force with output air pressure value is established. Finally, an experimental prototype is developed and an experimental test platform is built. Then, the comparison among the experimental data, simulation results and theoretical results verifies the capability of multi-DOF omnidirectional movement and flexible-actuated large load

Rime length, stress, and association domains

Every regular Chinese syllable has a syllable tone (the tone we get when the syllable is read in isolation). In some Chinese languages, the tonal pattern of a multisyllabic expression is basically a concatenation of the syllable tones. In other Chinese languages, the tonal pattern of a multisyllabic expression is determined solely by the initial syllable. I call the former M -languages (represented by Mandarin) and the latter S -languages (represented by Shanghai). I argue that there is an additional difference in rime structures between the two language groups. In S-languages, all rimes are simple, i.e., there are no underlying diphthongs or codas. In M-languages, all regular rimes are heavy. I further argue that a syllable keeps its underlying tones only if it has stress. Independent metrical evidence tells us that heavy rimes may carry inherent stress. Thus, in M-languages, all regular syllables are stressed and retain their underlying tones (which may or may not undergo further changes). In contrast, in S-languages, regular rimes do not carry inherent stress; instead, only those syllables that are assigned stress by rule can keep their underlying tones and hence head a multisyllabic tonal domain.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42998/1/10831_2005_Article_BF01440582.pd

Application of RFID and Mobile technology in Tracking of Equipment for Maintenance in the Mining Industry

Supply Chain Management (SCM) is a crucial factor in reducing the down time in equipment maintenance in the mining industry. The paper describes a tracking and verification system using Radio Frequency Identification (RFID) and mobile RFID technology developed for an SME in the UK to monitor the assembly of parts for a manufacturing company. The system uses low cost passive tags (costing few cents) to provide information in real time using TCP/IP protocol which is internet compatible and can be viewed anywhere in the organisation worldwide to provide more effective management control. The RFID technology and mobile RFID equipment is able to operate in a manufacturing and fabrication ‘metal environment’ with read/write distances of up to 6m. The information can be linked to a CAD system and/or Witness Quick 3D to provide visualisation and simulation of the shop floor in terms of equipment and personnel movement. This information can also be linked to digital imagery and used to provide evidence and visualisation for agile management systems in mining machinery workshops and stores

Applications of RFID and mobile technology in tracking of equipment for maintenance in the mining industry

Supply Chain Management (SCM) is a crucial factor in reducing the down time in equipment maintenance in the mining industry. The paper describes a tracking and verification system using Radio Frequency Identification (RFID) and mobile RFID technology developed for an SME in the UK to monitor the assembly of parts for a manufacturing company. The system uses low cost passive tags (costing few cents) to provide information in real time using TCP/IP protocol which is internet compatible and can be viewed anywhere in the organisation worldwide to provide more effective management control. The RFID technology and mobile RFID equipment is able to operate in a manufacturing and fabrication ‘metal environment’ with read/write distances of up to 6m. The information can be linked to a CAD system and/or Witness Quick 3D to provide visualisation and simulation of the shop floor in terms of equipment and personnel movement. This information can also be linked to digital imagery and used to provide evidence and visualisation for agile management systems in mining machinery workshops and stores

Knowledge Management Application of Internet of Things in Construction Waste Logistics with RFID Technology

The Internet of Things (IoT) is an emerging concept and that is currently under creation and development. However, it has already made an impact on many research domains by providing new solutions and ideas particularly in waste management and recycling. The IoT concept has provided a new research path that is conducive with public awareness of environmental protection considerations and improving recycling rates. This paper focuses on plasterboard waste as an example to propose a smart waste management framework. The 3 layers of the IoT model has been extended to 4-layers by splitting the application layer into knowledge management and visualization layer respectively. A smart waste management application has been developed, based on a case study of a local SME waste recycling company. This smart waste management system uses a service science approach, and it not only provides full logistical records for waste transportation but also provides waste collection arrangements and incident handling guidance to both management and operational staff