1D/2D Cobalt‐Based Nanohybrids as Electrocatalysts for Hydrogen Generation

The synergistic effects derived from optimizing the chemical and structural features of electrocatalysts permit them to attain remarkable activity and stability. Herein, 1D/2D cobalt‐based nanohybrid (CoNH) electrodes are developed; the structural design consists of Co₃O₄ electrospun nanoribbons (NRs) deposited onto a carbon fiber paper substrate where Co₃O₄ nanosheets are subsequently grown via an electrodeposition step and UV/ozone treatment. The content of noncovalently functionalized carbon nanotubes within the Co₃O₄ NRs is first tuned to enhance their charge transfer properties and mechanical stability. The electrocatalytic activity of the electrodes is further improved by a phosphorus modification of the 1D NRs, resulting in the formation of NaCoPO₄. The optimized 1D/2D CoNH electrode, i.e., ED‐0.09 wt% fCNTs/P‐CoNHs, displays a similar performance to that of platinum in 0.25 m Na₂S/0.35 m Na₂SO₃ (Tafel slope ≈102 mV dec^?1 for the former and ≈96 mV dec^?1 for the latter) and outstanding stability for up to 48 h. The versatility and high activity of this electrode is also demonstrated according to tests in a conventional water splitting system (cell voltage 1.55V, to produce 10 mA cm^?2) and a solar‐driven electrolyzer (1 m KOH).     

RiSeG: a ring based secure group communication protocol for resource-constrained wireless sensor networks

Securing group communication in wireless sensor networks has recently been extensively investigated. Many works have addressed this issue, and they have considered the grouping concept differently. In this paper, we consider a group as being a set of nodes sensing the same data type, and we alternatively propose an efficient secure group communication scheme guaranteeing secure group management and secure group key distribution. The proposed scheme (RiSeG) is based on a logical ring architecture, which permits to alleviate the group controller’s task in updating the group key. The proposed scheme also provides backward and forward secrecy, addresses the node compromise attack, and gives a solution to detect and eliminate the compromised nodes. The security analysis and performance evaluation show that the proposed scheme is secure, highly efficient, and lightweight. A comparison with the logical key hierarchy is preformed to prove the rekeying process efficiency of RiSeG. Finally, we present the implementation details of RiSeG on top of TelosB sensor nodes to demonstrate its feasibility.     

Improvement in monaural speech separation using sparse non-negative tucker decomposition

A monaural speech separation/enhancement technique based on non-negative tucker decomposition (NTD) has been introduced in this paper. In the proposed work, the effect of sparsity regularization factor on the separation of mixed signal is included in the generalized cost function of NTD. By using the proposed algorithm, the vector components of both target and mixed signal can be exploited and used for the separation of any monaural mixture. Experiment was done on the monaural data generated by mixing the speech signals from two speakers and, by mixing noise and speech signals using TIMIT and noisex-92 dataset. The separation results are compared with the other existing algorithms in terms of correlation of separated signal with the original signal, signal to distortion ratio, perceptual evaluation of speech quality and short-time objective intelligibility. Further, to get more conclusive information about separation ability, speech recognition using Kaldi toolkit was also performed. The recognition results are compared in terms of word error rate (WER) using the MFCC based features. Results show the average improved WER using proposed algorithm over the nearest performing algorithm is up to 2.7% for mixed speech of two speakers and 1.52% for noisy speech input.     

Manufacturing Data Analysis in Internet of Things/Internet of Data (IoT/IoD) Scenario

ABSTRACT

Computer integrated manufacturing (CIM) has enormous benefits as it increases the rate of production, reduces errors and production waste, and streamlines manufacturing sub-systems. However, there are some new challenges related to CIM operating in the Internet of Things/Internet of Data (IoT/IoD) scenarios associated with Industry 4.0 and cyber-physical systems. The main challenge is to deal with the massive volume of data flowing between various CIM components functioning in virtual settings of IoT. This paper proposes decisional DNA-based knowledge representation framework to manage the storage, analysis, and processing of data, information, and knowledge of a typical CIM. The framework utilizes the concept of virtual engineering object and virtual engineering process for developing knowledge models of various CIM components such as automatic storage and retrieval systems, automatic guided vehicles, robots, and numerically controlled machines. The proposed model is capable of capturing in real time the manufacturing data, information and knowledge at every stage of production, that is, at the object level, the process level, and at the factory level. The significance of this study is that it will support decision-making by reusing the experience, which will not only help in effective real-time data monitoring and processing, but also make CIM system intelligent and ready to function in the virtual Industry 4.0 environment.     

Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning

ABSTRACT

This work is part of an effort to develop of a knowledge–vision integration platform for hazard control in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment. The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting experiences from a new scenario, querying the user, and using their feedback to retrain the system through a hierarchical recognition structure using convolutional neural network (CNN). Thereafter, in contrast to the random sampling, the concept of active learning based on pruning of redundant points is tested. Results obtained from both random sampling and active learning are compared with a rigid systems that is not capable to aggregate new experiences as it runs. From the results obtained, it can be concluded that the classification accuracy improves greatly by adding new experiences, which makes it possible to customize the service according to each scenario and application as it functions. In addition, the active learning approach was able to reduce the user query and slightly improve the overall classification performance, when compared with random sampling.     

A pointcut-based coverage analysis approach for aspect-oriented programs

Aspect-oriented programming (AOP) is a promising technology that supports separation of crosscutting concerns (i.e., functionality that tends to be tangled with, and scattered through the rest of the system). In AOP, a method-like construct named advice is applied to join points in the system through a special construct named pointcut. This mechanism supports the modularization of crosscutting behavior; however, since the added interactions are not explicit in the source code, it is hard to ensure their correctness. To tackle this problem, this paper presents a rigorous coverage analysis approach to ensure exercising the logic of each advice - statements, branches, and def-use pairs - at each affected join point. To make this analysis possible, a structural model based on Java bytecode - called PointCut-based Def-Use Graph (PCDU) - is proposed, along with three integration testing criteria. Theoretical, empirical, and exploratory studies involving 12 aspect-oriented programs and several fault examples present evidence of the feasibility and effectiveness of the proposed approach.     

Supporting small teams in cooperatively building application domain models

Building application domain models is a time-consuming activity in software engineering. In small teams, it is an activity that involves almost all participants, including developers and domain experts. In our approach, we support the knowledge engineering activity by reusing tagging done by team participants when they search information on the Web about the application’s domain. Team participants collaborate implicitly when they do tagging because their individually created tags are collected and form a folksonomy. This folksonomy reflects their knowledge about the domain and it is the base for eliciting domain model elements in the knowledge acquisition and conceptualization tasks in a consensual way. Experiments provide evidence that our approach helps team participants to build richer domain models than if they do not use our software tool. The tool allows the reuse of simple annotations as long as users learn about the application’s domain.     

Virtual Engineering Object (VEO): Toward Experience-Based Design and Manufacturing for Industry 4.0

In this article we propose the concept, its framework, and implementation methodology for Virtual Engineering Objects (VEO). A VEO is the knowledge representation of an engineering object that embodies its associated knowledge and experience. A VEO is capable of adding, storing, improving, and sharing knowledge through experience. Moreover, it is demonstrated that VEO is a specialization of a Cyber-Physical System (CPS). In this article, it is shown through test models how the concept of VEO can be implemented with the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The test model confirmed that the concept of VEO is able to capture and reuse the experience of engineering artifacts, which can be beneficial for efficient decision-making in industrial design and manufacturing.     

Semantic Web and Social Web heading towards Living Documents in the Life Sciences

Rather than a document that is constantly being written as in the wiki approach, the Living Document (LD) is a document that also acts as a document router, operating by means of structured and organized social tagging and using existing ontologies. It offers an environment where users can manage papers and related information, share their knowledge with their peers and discover hidden associations amongst the shared knowledge. The LD builds upon both the Semantic Web, which values the integration of well-structured data, and the Social Web, which aims to facilitate interaction amongst people by means of user-generated content. In this vein, the LD is similar to a social networking system, with users as central nodes in the network, with the difference that interaction is focused on papers rather than people. Papers, with their ability to represent research interests, expertise, affiliations, and links to web based tools and databanks, are the central axis for interaction amongst users. To support this, we have also implemented a novel web prototype that enables researchers to accomplish three activities central to the Semantic Web vision: organizing, sharing and discovering. Availability: http://www.scientifik.info/livingdocument.