Biblio

The development of marine environment monitoring equipment has been improved by leaps and bounds in recent years. Numerous types of marine environment monitoring equipment have mushroomed with a wide range of high-performance capabilities. However, the existing data recording software cannot meet the demands of real-time and comprehensive data recording in view of the growing data types and the exponential data growth rate generated by various types of marine environment monitoring equipment. Based on the above-mentioned conundrum, this paper proposes a multi-source heterogeneous marine environmental data acquisition and storage method, which can record and replay multi-source heterogeneous data based upon the needs of real-time and accurate performance and also possess good compatibility and expandability.

MCI patients can be benefited from cognitive training programs to improve their cognitive capabilities or delay the decline of cognition. This paper evaluated three types of commonly seen categories of cognitive training programs (non-computerized / traditional cognitive training (TCT), computerized cognitive training (CCT), and virtual/augmented reality cognitive training (VR/AR CT)) based on six aspects: stimulation strength, user-friendliness, expandability, customizability/personalization, convenience, and motivation/atmosphere. In addition, recent applications of each type of CT were offered. Finally, a conclusion in which no single CT outperformed the others was derived, and the most applicable scenario of each type of CT was also provided.

Modular wireless power distribution systems will be commonly used in next generation factories to supply industrial production equipment, in particular automated guided vehicles. This requires the development of a flexible and standardized communication system in between individual Wireless Power Transfer (WPT) modules and production equipment. Therefore, we first derive the requirements for such a system in order to incorporate them in a generic communication concept. This concept focuses on the zero configuration and user-friendly expandability of the system, in which the communication unit is integrated in each WPT module. The paper describes the communication concept and discusses the advantages and disadvantages. The work concludes with an outlook on the practical implementation in a research project.

Under the background of rapid development of campus informatization, the information management of college students' innovative activities is slightly outdated, and the operation of the traditional innovative activity record system has gradually become rigid. In response to this situation, this paper proposes a B/S architecture-based information management system for college students' innovative activities based on the current situation that the network and computers are widely used, which is designed for the roles of relevant managers of students on campus, such as class teachers, teachers and counselors, and has developed various functions to meet the needs of such users as class teachers, including user The system is designed to meet the needs of classroom teachers, classroom teachers and tutors. In order to meet the requirements of generality, expandability and ease of development, the overall architecture of the system is based on the javaEE platform, with JSP technology as the main development technology.

Any Big Data scenario eventually reaches scalability concerns for several factors, often storage or computing power related. Modern solutions have been proven to be effective in multiple domains and have automated many aspects of the Big Data pipeline. In this paper, we aim to present a solution for deploying event-based automated data processing tools for low code environments that aim to minimize the need for user input and can effectively handle common data processing jobs, as an alternative to distributed solutions which require language specific libraries and code. Our architecture uses a combination of a network exposed service with a cluster of “Data Workers” that handle data processing jobs effectively without requiring manual input from the user. This system proves to be effective at handling most data processing scenarios and allows for easy expandability by following simple patterns when declaring any additional jobs.

Multi-active bridge (MAB) converter has played an important role in the power conversion of renewable-based smart grids, electrical vehicles, and more/all electrical aircraft. However, the increase of MAB submodules greatly complicates the control architecture. In this regard, the conventional centralized control strategies, which rely on a single controller to process all the information, will be limited by the computation burden. To overcome this issue, this paper proposes a decentralized power management strategy for MAB converter. The switching frequencies of MAB submodules are adaptively regulated based on the submodule local information. Through this effort, flexible electrical power routing can be realized without communications among submodules. The proposed methodology not only relieves the computation burden of MAB control system, but also improves its modularity, flexibility, and expandability. Finally, the experiment results of a three-module MAB converter are presented for verification.

This paper reports on the advanced assembly technology for small chip size of Fan-out WLP(FO-WLP) using high expansion tape. In a preceding paper, we reported that we have developed new tape expansion machine which can expand tape in four directions individually. Using this expansion machine device, we have developed high expansion tape which can get enough chip distance after expansion. Our expansion technology provides both high throughput and high placement accuracy. These previous studies have been evaluated using 3 mm x 3 mm chips assuming an actual FO-WLP device. Since our process can be handled by wafer size, smaller chip size improves throughput than larger chip size. In this study, we evaluate with 0.6 mm x 0.3 mm chip size and investigate tape characteristics required for small chip size expansion. By optimizing adhesive thickness and composition of adhesive, we succeed in developing high expansion tape for small chip size with good expandability and no adhesive residue on the expanded chip. We indicate that our proposal process is also effective for small chip size of FO-WLP.

Due to the intermittent nature of renewable energy sources, the implementation of energy storage systems (ESSs) is crucial for the reliable operation of microgrids. This paper proposes a peer-to-peer distributed secondary control scheme for accurate voltage restoration of distributed ESS units in a DC microgrid. The presented control framework only requires local and neighboring information to function. Besides, the ESSs communicate with each other through a sparse network in a discrete fashion compared to existing approaches based on continuous data exchange. This feature ensures reliability, expandability, and flexibility of the proposed strategy for a more practical realization of distributed control paradigm. A simulation case study is presented using MATLAB/Simulink to illustrate the performance and effectiveness of the proposed control strategy.

Various approaches of microgrid operation have been proposed, albeit with noticeable issues such as power-sharing, control of frequency and voltage excursions, applicability on different grids, etc. This paper proposes a goal function-based, decentralized control that addresses the mentioned problems and secures the microgrid stability by constraining the frequency and node deviations across the grid while simultaneously supporting the desired active power exchange between prosumer nodes. The control algorithm is independent of network topology and enables arbitrary node connection, i.e. seamless microgrid expandability. To confirm the effectiveness of the proposed control strategy, simulation results are presented and discussed.

This paper introduces lronMask, a new versatile verification tool for masking security. lronMask is the first to offer the verification of standard simulation-based security notions in the probing model as well as recent composition and expandability notions in the random probing model. It supports any masking gadgets with linear randomness (e.g. addition, copy and refresh gadgets) as well as quadratic gadgets (e.g. multiplication gadgets) that might include non-linear randomness (e.g. by refreshing their inputs), while providing complete verification results for both types of gadgets. We achieve this complete verifiability by introducing a new algebraic characterization for such quadratic gadgets and exhibiting a complete method to determine the sets of input shares which are necessary and sufficient to perform a perfect simulation of any set of probes. We report various benchmarks which show that lronMask is competitive with state-of-the-art verification tools in the probing model (maskVerif, scVerif, SILVEH, matverif). lronMask is also several orders of magnitude faster than VHAPS -the only previous tool verifying random probing composability and expandability- as well as SILVEH -the only previous tool providing complete verification for quadratic gadgets with nonlinear randomness. Thanks to this completeness and increased performance, we obtain better bounds for the tolerated leakage probability of state-of-the-art random probing secure compilers.

This paper proposes the modular power conversion systems based on an Universal Smart Power Module (USPM). In this concept, the Power Electronics Building Block (PEBB) is improved the flexibility and the expandability by integrating a high-speed power electronics controller, input/output filters among each USPM to realize the simplification of the power electronics design. The original point of USPM is that each power module operates independently because a high-speed power electronics controller is implemented on each power module. The power modules of PEBB are typically configured by the main power circuits and the gate driver. Therefore, the controller has to be designed specifically according to various applications although the advantages of PEBB are high flexibility and user-friendly. The contribution of USPM is the simplification of the system design including power electronics controller. On the other hand, autonomous distributed systems require the control method to suppress the interference in each module. In this paper, the configuration of USPM, example of the USPM system, and detail of the control method are introduced.

An open source software package for performing dynamic RMS simulation of small to medium-sized power systems is presented, written entirely in the Python programming language. The main objective is to facilitate fast prototyping of new wide area monitoring, control and protection applications for the future power system by enabling seamless integration with other tools available for Python in the open source community, e.g. for signal processing, artificial intelligence, communication protocols etc. The focus is thus transparency and expandability rather than computational efficiency and performance.The main purpose of this paper, besides presenting the code and some results, is to share interesting experiences with the power system community, and thus stimulate wider use and further development. Two interesting conclusions at the current stage of development are as follows:First, the simulation code is fast enough to emulate real-time simulation for small and medium-size grids with a time step of 5 ms, and allows for interactive feedback from the user during the simulation. Second, the simulation code can be uploaded to an online Python interpreter, edited, run and shared with anyone with a compatible internet browser. Based on this, we believe that the presented simulation code could be a valuable tool, both for researchers in early stages of prototyping real-time applications, and in the educational setting, for students developing intuition for concepts and phenomena through real-time interaction with a running power system model.

Incipient faults are faults at their initial stages and occur before permanent faults occur. It is very important to detect incipient faults timely and accurately for the safe and stable operation of the power system. At present, most of the detection methods for incipient faults are designed for the detection of a single device’s incipient fault, but a unified detection for multiple devices cannot be achieved. In order to increase the fault detection capability and enable detection expandability, this paper proposes a waveform vector embedding (WVE) method to embed incipient fault waveforms of different devices into waveform vectors. Then, we utilize the waveform vectors and formulate them into a waveform dictionary. To improve the efficiency of embedding the waveform signature into the learning process, we build a loss function that prevents overflow and overfitting of softmax function during when learning power system waveforms. We use the real data collected from an IEEE Power & Energy Society technical report to verify the feasibility of this method. For the result verification, we compare the superiority of this method with Logistic Regression and Support Vector Machine in different scenarios.

The front-end text processing module is considered as an essential part that influences the intelligibility and naturalness of a Mandarin text-to-speech system significantly. For commercial text-to-speech systems, the Mandarin front-end should meet the requirements of high accuracy and low time latency while also ensuring maintainability. In this paper, we propose a universal BERT-based model that can be used for various tasks in the Mandarin front-end without changing its architecture. The feature extractor and classifiers in the model are shared for several sub-tasks, which improves the expandability and maintainability. We trained and evaluated the model with polyphone disambiguation, text normalization, and prosodic boundary prediction for single task modules and multi-task learning. Results show that, the model maintains high performance for single task modules and shows higher accuracy and lower time latency for multi-task modules, indicating that the proposed universal front-end model is promising as a maintainable Mandarin front-end for commercial applications.

In this work, an IF(intermediate frequency) module of a hyperspectral microwave radiometer based on a polyphase filter bank (PFB) and Discrete Fourier Transformation (DFT)is introduced. The IF module is designed with an 800MSPS sampling-rate ADC and a Xilinx Virtex-7 FPGA. The module can achieve 512 channels and a bandwidth of 400M and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, accuracy, etc. It can be used in various applications of microwave remote sensing. The system has strong expandability.

Accidents in Nuclear Power Plants (NPPs) can occur for a variety of causes. However, among these, the scale of accidents due to human error can be greater than expected. Accordingly, researches are being actively conducted using artificial intelligence to reduce human error. Most of the research shows high performance based on the numerical data on NPPs, but the expandability of researches using only numerical data is limited. Therefore, in this study, abnormal diagnosis was performed using artificial intelligence based on image data. The methods applied to abnormal diagnosis are the deep neural network, convolution neural network, and convolution recurrent neural network. Consequently, in nuclear power plants, it is expected that the application of more methodologies can be expanded not only in numerical data but also in image-based data.

With the rapid development of e-commerce and the increasing popularity of credit cards, online transactions have become increasingly smooth and convenient. However, many online transactions suffer from credit card fraud, resulting in huge losses every year. Many financial organizations and e-commerce companies are devoted to developing advanced fraud detection algorithms. This paper presents an approach to detect fraud transactions using the IEEE-CIS Fraud Detection dataset provided by Kaggle. Our stacked model is based on Gradient Boosting, LightGBM, CatBoost, and Random Forest. Besides, implementing StackNet improves the classification accuracy significantly and provides expandability to the network architecture. Our final model achieved an AUC of 0.9578 for the training set and 0.9325 for the validation set, demonstrating excellent performance in classifying different transaction types.

Vision systems, i.e., systems that enable the detection and tracking of objects in images, have gained substantial importance over the past decades. They are used in quality assurance applications, e.g., for finding surface defects in products during manufacturing, surveillance, but also automated driving, requiring reliable behavior. Interestingly, there is only little work on quality assurance and especially testing of vision systems in general. In this paper, we contribute to the area of testing vision software, and present a framework for the automated generation of tests for systems based on vision and image recognition with the focus on easy usage, uniform usability and expandability. The framework makes use of existing libraries for modifying the original images and to obtain similarities between the original and modified images. We show how such a framework can be used for testing a particular industrial application on identifying defects on riblet surfaces and present preliminary results from the image classification domain.

Wireless embedded devices are key elements of Internet-of-Things (IoT) and industrial IoT (IIoT) applications. The complexity of these devices as well as the number of connected devices to networks increase steadily. The high intricacy of the overall system makes it error-prone and vulnerable to attacks and leads to the need to test individual parts or even the whole system. Therefore, this paper presents the concept of a flexible and distributed testbed to evaluate correct behavior in various operation or attack scenarios. It is based on the Robot Operating System (ROS) as communication framework to ensure modularity and expandability. The testbed integrates RF-jamming and measurement devices to evaluate remote attack scenarios and interference issues. An energy harvesting emulation cell is used to evaluate different real-world energy harvesting scenarios. A climatic test chamber allows to investigate the influence of temperature and humidity conditions on the system-under-test. As a testbed application scenario, the automated evaluation of an energy harvesting wireless sensor network designed to instrument automotive engine test benches is presented.

Radio is the most important part of any wireless network. Radio Access Network (RAN) has been virtualized and disaggregated into different functions whose location is best defined by the requirements and economics of the use case. This Virtualized RAN (vRAN) architecture separates network functions from the underlying hardware and so 5G can leverage virtualization of the RAN to implement these functions. The easy expandability and manageability of the vRAN support the expansion of the network capacity and deployment of new features and algorithms for streamlining resource usage. In this paper, we try to address the problem of scheduling 5G vRAN with mid-haul network capacity constraints as a combinatorial optimization problem. We transformed it to a Quadratic Unconstrained Binary Optimization (QUBO) problem by using a newly proposed quantum-based algorithm and compared our implementation with existing classical algorithms. This work has demonstrated the advantage of quantum computers in solving a particular optimization problem in the Telecommunication domain and paves the way for solving critical real-world problems using quantum computers faster and better.

Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min-max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k-nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k-nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification.

A full-duplex self-recovery optical fibre transport system is proposed on the basis of a novel passive single-line bidirectional optical add/drop multiplexer (SBOADM). This system aims to achieve an access network with low complexity and network protection capability. Polarisation division multiplexing technique, optical double-frequency application and wavelength reuse method are also employed in the transport system to improve wavelength utilisation efficiency and achieve colourless optical network unit. When the network comprises a hybrid tree-ring topology, the downstream signals can be bidirectionally transmitted and the upstream signals can continuously be sent back to the central office in the reverse pathways due to the remarkable routing function of the SBOADM. Thus, no complicated optical multiplexer/de-multiplexer components or massive optical switches are required in the transport system. If a fibre link failure occurs in the ring topology, then the blocked network connections can be recovered by switching only a single optical switch preinstalled in the remote node. Simulation results show that the proposed architecture can recover the network function effectively and provide identical transmission performance to overcome the impact of a breakpoint in the network. The proposed transport system presents remarkable flexibility and convenience in expandability and breakpoint self-recovery.

Compared with traditional centralized control strategies, the distributed control systems significantly improve the flexibility and expandability of an modular multilevel converter (MMC). However, the stability issue in the MMC distributed control system with the presence of control loop coupling interactions is rarely discussed in existing research works. This article is to improve the stability of an MMC distributed control system by inhibiting the control conflict due to the coupling interactions among control loops with incomplete control information. By modeling the MMC distributed control system, the control loop coupling interactions are analyzed and the essential cause of control conflict is revealed. Accordingly, a control parameter design principle is proposed to effectively suppress the disturbances from the targeted control conflict and improve the MMC system stability. The rationality of the theoretical analysis and the effectiveness of the control parameter design principle are confirmed by simulation and experimental results.

Clustering is a concept in data mining, which divides a data set into different classes or clusters according to a specific standard, making the similarity of data objects in the same cluster as large as possible. Clustering by fast search and find of density peaks (DPC) is a novel clustering algorithm based on density. It is simple and novel, only requiring fewer parameters to achieve better clustering effect, without the requirement for iterative solution. And it has expandability and can detect the clustering of any shape. However, DPC algorithm still has some defects, such as it employs the clear neighborhood relations to calculate local density, so it cannot identify the neighborhood membership of different values of points from the distance of points and It is impossible to accurately cluster the data of the multi-density peak. The fuzzy neighborhood density peak clustering algorithm is proposed for this shortcoming (F-DPC): novel local density is defined by the fuzzy neighborhood relationship. The fuzzy set theory can be used to make the fuzzy neighborhood function of local density more sensitive, so that the clustering for data set of various shapes and densities is more robust. Experiments show that the algorithm has high accuracy and robustness.

This paper proposes a hardware Self-Organizing Map (SOM) for high dimensional vectors. The proposed SOM is based on nested architecture with pipeline processing. Due to homogeneous modular structure, the nested architecture provides high expandability. The original nested SOM was designed to handle low-dimensional vectors with fully parallel computation, and it yielded very high performance. In this paper, the architecture is extended to handle much higher dimensional vectors by using sequential computation, which requires multiple clocks to process a single vector. To increase the performance, the proposed architecture employs pipeline computation, in which search of winner neuron and weight vector update are carried out simultaneously. Operable clock frequency for the system was 60 MHz, and its throughput reached 15012 million connection updates per second (MCUPS).