Incorporating semantics in modern projects can be time-consuming, rely on limited re-sources, and require expert knowledge to implement in a future-proof manner. Across many working groups and industrial fields, semantics continues to help solve data consolidation challenges; however, there are still emerging situations such as where modeling and ontology engineering efforts overlap, ongoing siloed data, or even continuing to create tech-debt for the future. Tools have been developed and researched that offer users assistance with the automation of select expert tasks to help streamline semantic artifact creation, understanding of design decisions, and domain data alignments. The Semantic Knowledge Graph Generator and Recommendation Framework encourage semantic projects that are automated from the start to follow industry standards, enhanced with recommendations, and use components which make semantic artifacts more accessible for non-semantic experts. Based on that tool, this extension article presents several industry use cases and related discussion where semantic recommendations and deployment automation have assisted with project development, teaching about ontology structures, and gaining accessibility of semantic artifacts.

Semantic technology continues to advance but adoption still has existing and emerging challenges due to varying amounts of individual knowledge for performing development tasks, limited semantic-expert resources, as well as fast-paced time lines to produce industry products. Semantic modeling is usually time-consuming even for those with the expertise. Ad-hoc and rushed implementations may result in less ideal interoperability in downstream work, slowing down adoption of knowledge graphs in intelligent systems. The objective of this contribution is to use automation of certain expert tasks to propel semantic development efforts toward solutions that avoid compounding semantic tech debt for data alignment-based tasks and subsequent knowledge graph-based projects. This work aims to function in tandem with other existing internal data curation and deployment efforts to offer an automation-based framework and tool with results automatically deployable to a chosen project space, semantic editor, or triple store. This ultimately encourages development with artifacts that have already been assembled with all semantic dependencies to encourage using them alongside other industry standards. It also reduces expensive and redundant modeling efforts, helps to teach non-semantic experts about the implications of their modeling choices with recommendations and paradigm delineations, and eases remaining data alignment work so that results more easily follow semantic best practices.

Fault diagnosis is critical for intelligent manufacturing by monitoring the status of a production line and preventing financial loss. Model-based fault diagnosis has the advantage of being able to explain the cause and propagation of faults over model-free diagnosis, but would need knowledge about the configuration model and context-specific information of the production line. Ontology modelling can provide context-specific information on top of a configuration model to benefit fault diagnosis. Typically ontologies are manually constructed and then used by a reasoner based on a set of predefined rules. From the perspective of fault diagnosis, this approach works as an expert system where both the ontology models and predefined rules are specific to a given system. Once the system has changed which happens from time to time as repairs and updates in a production line, or in the case of a different system, the ontology models and predefined rules would need to be manually modified or reconstructed. Here a model-based method is proposed to automate generation of configuration models with context-specific information using semantic web technology when a production line is healthy, and to use the generated configuration model and information for diagnosis when the production line has a fault. The method does not rely on predefined rules and reasoners, but rather uses dynamics models that are based on first-principle qualitative mechanics. It uses numerical optimization to minimize the discrepancy between sensor data from the production line and from simulation running the dynamics model to achieve automatic configuration modelling and fault diagnosis. With three use cases commonly found for a production line, i.e. automatic sensor placement modeling or misplacement diagnosis, motor fault diagnosis with single sensor modality, and motor fault diagnosis with sensory substitution, the feasibility of the proposed method is demonstrated. The method's faster computational speed and comparable accuracy to a quantitative model-based approach suggests it may complement and accelerate the latter with early-stage selection of candidate models for both modelling and fault diagnosis.

The industrial domain offers a high degree of standardization, a variety of very specialized use cases, and an abundance of resources. These characteristics provide perfect conditions for Digital Companion systems. A Digital Companion is a cognitive agent that assists human users by taking on three roles: as guardians, assistants or mentors, and partners. This paper describes the characteristics, conceptual architecture, use cases and open challenges regarding Digital Companions for industry.

We present HoloMiracle, a system that enables operators of industrial equipment and beyond to pose queries about physical, virtual, regulatory, and functional relationships between components of the equipment and that visualizes the responses to their queries in-situ, as a holographic overlay. We report on HoloMiracle's system architecture and discuss a concrete use case in the automotive manufacturing domain.

This article introduces the Open Semantic Framework (OSF), a one-stop shop for creating and deploying semantic applications as well as their lifecycle management. The authors discuss how the OSF supports knowledge acquisition into semantic knowledge models via novel interface technologies, manages these models internally within core ontologies and pluggable knowledge packs, and provides moderated access to them via a REST API and prefabricated SPARQL queries. As an example of OSF's usage in a practical scenario, the authors show how it can increase worker safety in industrial settings by mitigating workplace safety hazards through the automatic insertion of safety-relevant actions directly in the workflow, based on a model of applicable workplace safety law and regulation. By facilitating the creation, integration, and provisioning of knowledge, the OSF represents an important step on the way to integrating knowledge models worldwide, enabling semantic applications globally to "stand on the shoulders of giants."

This study presents a two-dimensional (2D) scanning antenna array of ultra high frequency radio frequency identification system with high stability and reliability in metal cavity. The antenna array is composed of nine elements and a feeding network. The two-shorting-plate planar inverted-F antenna is designed as antenna element with small dimension 10 × 10 × 1.5 cm3. The bandwidth of element is about 72 MHz (840–912 MHz) under the condition of voltage standing wave ratio (VSWR) <2. The feeding network consists of a butler matrix and three three-way power dividers. Both the nine antenna elements and a feeding network are specified within 33 × 30 × 20 cm3 areas, where switches are used to change the connection of feeding network to obtain 2D scanning beam. The measured bandwidth is about 30 MHz (845–875 MHz) under the condition of VSWR <2, which covers Europe standard (865–868 MHz) completely. The scanning beam directions are ±60° with 10 dBi gain at the xz-plane and ±50° with 10 dBi gain at the yz-plane. The read rate of the proposed antenna array achieves 100% in the metal cavity with 18 dBm input power.

针对非理想信道状态信息下两跳中继多输入多输出(MIMO)系统,按照空间相关性进行了非理想MIMO信道建模,在此基础上提出了一种新的多节点联合优化设计该系统收发信机的算法.该算法基于估计信号的最小均方误差(MMSE)准则,把源节点预编码、中继节点预编码以及目的节点均衡算法结合起来进行三节点联合优化,提高了通信系统的可靠性.收敛性和复杂度的仿真分析表明,该三节点联合优化算法与传统的中继和目的节点两节点迭代联合优化算法相比较,误码率有1dB以上的增益,复杂度大致相当;如果该算法退化为两节点的话,虽然在性能上与文献给出的方法大致相当,但是复杂度可以降低30％左右.该算法复杂度的降低将会以占用存储资源为代价,因为在计算过程中很多矩阵会被重用,中间变量需要存储下来,但从综合衡量性能与复杂度的角度看,该方法具有一定程度的优势.

In the current UHF RFID standards, the data transfer on reverse link, from tag to reader, is unreliable in nature since there is only error detection code (CRC) used. With the continuous improvement of sensitivity for tag chip, the unreliable reverse link may become the bottleneck for the whole system. In this paper, a nested error correcting code (ECC) based data transfer method is proposed to enhance the reliability of reverse link while maintaining the acceptable read efficiency. The information bits are encoded in the nested way to get the redundant bits for each layer. The reader writes the original information bits and all redundant bits into the tag. During the reading process, the reader gets the redundant bits at different layer in an incremental manner. The purpose is to correct the errors by requiring as less redundant bits as possible. According to the numerical results, the reliability in terms of residual error probability is improved significantly compared to the normal scheme with only CRC check. However, the efficiency in terms of average time to get information bits is much better than the single code protection scheme. Therefore, the proposed scheme can get a good tradeoff between reliability and efficiency. On the other hand, the proposed scheme can be implemented in software, requiring no modifications on the reader hardware and tag.

Automatic goods inventory on the shelf is an important Ultra High Frequency (UHF) Radio Frequency Identification (RFID) application. Compared to other applications, it has some unique requirements: 1) Confined read region to avoid the cross reads to other tiers and shelves; 2) Positioning capability to know where along the shelf the tag is; 3) Low total system cost to support many shelves in the application. In this paper, an innovative UHF RFID solution is proposed for the RFID shelf. It contains two key technologies. First, the proposed near field (NF) shelf antenna in [1] is improved to provide the possibility to connect 5 cascaded antennas to one antenna port. It can save the system cost by reducing the number of needed readers. Second, a phase difference measurement based tag positioning method is proposed. To support different applications, it can provide both tier level and item level positioning for the goods on the shelf. Based on the measurement results, the proposed method can get the item level positioning accuracy with an average error of 4.16cm. It can also get 100% tier level positioning accuracy.

A novel adaptive power control and beam-forming joint optimization algorithm is proposed in cognitive radio (CR) underlay networks, where cognitive network share spectrum with primary network which spectrum is licensed. In this paper, both primary base station (PBS) and cognitive base station (CBS) are all equipped with multi antennas, while each primary user (PU) and cognitive user (CU) has only one antenna. Different from traditional algorithms, an adaptive weight factor generating solution is supplied to different access users (both PUs and CUs) in this paper, and the different priority of users is also considered, because PUs have higher priority, the weight factor of PUs is fixed as constant and signal-to-interference and noise ratio (SINR) threshold is unchanged, while for CUs, it is set adaptively and SINR threshold is also changed accordingly. Using this algorithm, the transmit power is decreased, which relax the strict requirements for power amplifier in communication systems. And moreover, owing to PUS has fixed SINR threshold, the calculated SINR at receiver is nearly unchanged, but for CUs, the SINR is changing with the adaptive weight factor. Under the assurance of quality of service (QoS) of PUs, the solution in this paper can enable CRs access to the CR network according to adaptive SINR threshold, therefore which supplies higher spectrum utilization efficiency.

A novel equalization and precoding joint optimization algorithm is proposed in dual hop multiple input multiple output (MIMO) relay communication systems. This solution combines the equalization for the first phase and precoding for the second phase in relay system, therefore the joint optimization can be equaled with the unique optimization for the product matrix of detection and precoding, which avoid the non convergence problems in iterating process, compared with iterative optimization, the complexity burden of this algorithm is greatly reduced. And from simulation results the performance is only a little degraded. Therefore, when complexity and performance are considered, this algorithm reached a better compromise.

Based on its' merits of fast tag access speed and long read distance, UHF RFID technology is regarded as a promising technology for both logistics applications and manufacturing applications. By using the electromagnetic wave propagation method in the far field, UHF RFID system gets large impacts from the field nulls and the interferences. The system can only provide satisfactory service under the correct configurations. The configurations may contain the reader communication parameters and the type, position/direction of the reader's antenna and the tag's antenna. Especially in the application scenarios with multiple readers deployed, the reader-to-reader (R2R) interference and the reader-to-tag (R2T) interference must be carefully dealt with to get the good system performance. To help the system integrator configure the system efficiently, a reader planning tool is developed to provide the visualized coverage map under different system configurations. In this paper, the internal design of this tool will be discussed. First, in the scenario with one reader deployed, the communications on the forward link (from reader to tag) and the reverse link (from tag to reader) will be analyzed to get the read probability map. Then, the R2R interference and the R2T interference will be analyzed in the scenarios with multiple readers deployed. This tool is already widely used by the system integrators in many real applications.