The proliferation of decentralized applications across different autonomous blockchains raises the need to enable cross-chain data interoperability (CCDI). However, prior approaches for supporting CCDI often hit scalability bottlenecks regarding critical metrics, e.g., memory, or remain prone to withholding and censorship attacks. This paper proposes two protocols to implement secure and efficient CCDI under adversarial conditions. The cross-chain token exchange (CCTE) protocol for atomic swaps is proposed. It adopts a deposit mechanism, a blockchain-of-blockchains (BoB), and Merkle proofs to ensure the completion of token exchanges even under withholding attacks. It utilizes a parallelized design to support concurrent token exchanges, thereby improving its efficiency and avoiding censorship attacks that target sequential token exchanges. The CCDI protocol is proposed to support any CCDI application. It authorizes a unique BoB to execute arbitrary CCDI application logic. It integrates a “transfer and in place data update” mechanism to improve its efficiency, and this mechanism enables a blockchain update its state data items using a single transaction, without requiring any information from other blockchains. Moreover, the CCDI protocol integrates a state data migration scheme, which supports a user to migrate its state data item to censorship-resilient blockchains, and incorporates a malicious user nodes elimination scheme, which enables the updates of state data items in a CCDI process even under withholding attacks. Systematic performance evaluations are conducted to compare the two protocols with existing ones. The CCTE protocol reduces latency by at least 52% compared to existing protocols under probabilistic consensus setting. The CCDI protocol outperforms prior protocols, lowering communication cost by 59%, computation overhead by 41%, memory burden by 12%, and latency cost by 33%.

In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO–DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., “planning,” “operation,” “market,” “asset,” and “connection”) with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.

The integration of soft open point (SOP) effectively improves the flexibility of active distribution networks (ADNs). However, in practical operation, accurate network parameters are difficult to obtain and the operation state changes rapidly with distributed generators (DGs). With the development of information technologies, massive operation data can be acquired in ADNs. How to utilize multisource data has become the key to realize the intelligent operation of ADNs. This article proposes a data-driven operation strategy of SOP based on model-free adaptive control (MFAC). First, considering the inaccurate parameters and frequent change of operation states, a data-driven framework is formulated for the real-time operation of SOP. Then, the operation strategies of multiple SOPs are further improved with interarea coordination. The results of case studies show that driven by the measurement data, the potential benefits of SOPs are explored to adaptively respond to system state changes and improve the operational performance of ADNs.

Owners of combined heat and power (CHP), e.g., industrial manufacturers, are motivated to provide frequency response to power grids due to clear financial benefits. Yet, the slow response speed of CHP limits its capability in providing such services. Moreover, frequent adjustments would cause a faster lifetime reduction of CHP and rapid pressure fluctuation in the gas network. To further unlock the flexibility of CHP, this article integrates a battery unit with CHP via a power electronic interface. A filter-based coordinative controller is designed for smoothing short-term fluctuations in CHP outputs. Based on the filter parameters and frequency response requirements, the minimum required capacity of the battery is identified. The results show that the proposed system enhances the capability of CHP for frequency response and mitigates the associated adverse effects on the gas network. The required capacity of the battery is economically feasible considering the benefit it brings to the CHP.

Three-phase four-wire inverters are usually used to feed unbalanced three-phase loads with neutral currents. The unbalanced three-phase loads also bring to second-order ripples in the DC bus, which should be mitigated by bulky DC-bus capacitors to improve the system performance. In this case, the DC capacitance is designed for the second-order ripple frequency instead of the switching frequency, so it can not be reduced even when SiC MOSFETs are adopted to achieve high switching frequency. Although various topologies of three-phase four-wire inverters has been proposed to provide the path for neutral currents, they cannot handle the second-order ripples. Also, some active power decoupling solutions can be adopted, but they require additional active swithes and components, which increases the cost of the system. In this paper, a four-leg buck inverter is proposed, which consists of four DC-DC buck converters. Each buck converter is independently controlled. This topology can not only provide neutral currents, but also reduce the second-order ripples in the DC bus with active power decoupling control. The proposed topology doesn't require any additional active switches comparing to the conventional topologies with neutral legs. The effectiveness of proposed topology is verified by the simulation in MATLAB/Simulink.

SiC-based inverters can operate at high switching frequency with high efficiency, which can reduce the size of passive components and heat sinks to achieve high power density. However, in three-phase four-wire inverters supplying unblanced loads, the second-order ripples in the DC bus need to be mitigated by large DC capacitance, which increases the size of the converter. The conventional neutral leg is widely used in three-phase four-wire inverters to provide neutral currents for the unbalanced loads. In this paper, an improved neutral leg is proposed, which can provide neutral currents and reduce the second-order ripples in the DC bus simultaneously. The DC bus ripples can be reduced without adding any hardware components. Furthermore, the proposed neutral leg can save 50% DC capacitance comparing to the conventional neutral leg. The proposed neutral leg was built with SiC MOSFETs and tested with a three-phase four-wire inverter in the laboratory. The experimental results verified the effectiveness of the proposed neutral leg.

Increasing penetration of renewable generation results in lower inertia of electric power systems. To maintain the system frequency, system operators have been designing innovative frequency response products. Enhanced frequency response (EFR) newly introduced in the UK is an example with higher technical requirements and customized specifications for assets with energy storage capability. In this paper, a method was proposed to estimate the EFR capacity of a population of industrial heating loads, bitumen tanks, and a decentralized control scheme was devised to enable them to deliver EFR. Case study was conducted using real UK frequency data and practical tank parameters. Results showed that bitumen tanks delivered high-quality service when providing service-1-type EFR, but underperformed for service-2-type EFR with much narrower deadband. Bitumen tanks performed well in both high- and low-frequency scenarios, and had better performance with significantly larger numbers of tanks or in months with higher power system inertia.

Selection of the most appropriate communications technology for a smart grid (SG) application is far from trivial. We propose such a feasibility assessment starting from identification of key performance indicators (KPIs) required for peer-to-peer (P2P) energy trading and grid control operations from a communications perspective. A set of cross-disciplinary KPIs, both quantitative and qualitative, are considered from communications, power, business, actor involvement, financial, and demand side management categories. They serve as a general baseline for use cases, as there have been few previous works attempting to capture the essential features of P2P SG operations. The KPIs are briefly identified along with their relations to P2P energy trading and grid control. A straightforward comparison of the quantitative and qualitative KPIs' impact on technology selection is not feasible. This paper addresses the comparison with: 1) a prioritization of the KPIs using the analytic hierarchy process; 2) a comparison of technology solutions evaluated in our previous works against the KPIs' requirements; and 3) a total feasibility evaluation of the solutions against selected KPIs. The prioritization shows latency, reliability, security, scalability, robustness, costs of information and communication technologies (ICT) devices, and costs of ICT deployment are the most important KPIs in enabling P2P energy trading and grid control. Further, the technology feasibility assessment enables identification of the most suitable candidates for an SG application.

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of "Deep Learning" strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.

Being the core course of the computer science, the teaching of "the Principle of Computer Organization", concerning its training purpose and training objective, should focus on the nurturing of the students' ability of discovering, analyzing and solving problems with an overall perspective of the course, so as to lay a solid foundation for the systematic wholeness of computer science, and improve the students' professional ability as well. And the concept of course-group is also proposed in the paper, which means the joint construction of the correlated courses under the frame of computer science syllabus of unified planning, cooperation and improvement.

A practical postal numeral segmentation and recognition system for Chinese business letters is presented. Line information for the address blocks is gained from the envelope image by projection, then the sub-image of the postal numerals is segmented. The numerals are recognized by three independent classifiers, and their results are combined to form final results. An improved voting principle based on a priori knowledge of each classifier is utilized to combine multiple classifiers in order to achieve perfect performance for high recognition rate and low error rate. The recognition ability of each classifier, acting with the voting principle, is obtained by statistics for a large set of samples. Different thresholds gained by training are employed for different classes, which can yield better results. A parallel structure hardware is designed to implement a real-time image processing system. The performance of the system is shown to be satisfactory, sufficient for the requirements of practical use.