Biblio

In this paper, a generic model for a differential stripline is created using machine learning (ML) based regression analysis. A recursive approach of creating various inputs is adapted instead of traditional design of experiments (DoE) approach. This leads to reduction of number of simulations as well as control the data points required for performing simulations. The generic model is developed using 48 simulations. It is comparable to the linear regression model, which is obtained using 1152 simulations. Additionally, a tabular W-element model of a differential stripline is used to take into consideration the frequency-dependent dielectric loss. In order to demonstrate the expandability of this approach, the methodology was applied to two differential pairs of striplines in the frequency range of 10 MHz to 20 GHz.

Series DC electric springs (DCESs) are a state-of-the-art demand-side management (DSM) technology with the capability to reduce energy storage requirements of DC microgrids by manipulating the power of non-critical loads (NCLs). As the stability of DC microgrids is highly prone to dynamic interactions between the system active and passive components, this study intends to conduct a comprehensive small-signal stability analysis of a community DC microgrid integrated with distributed DCESs considering the effect of destabilizing constant power loads (CPLs). For this purpose, after deriving the small-signal model of a DCES-integrated microgrid, the sensitivity of the system dominant frequency modes to variations of various physical and control parameters is evaluated by means of eigenvalue analysis. Next, an active damping control method based on virtual RC parallel impedance is proposed for series DCESs to compensate for their slow dynamic response and to provide a dynamic stabilization function within the microgrid. Furthermore, impedance-based stability analysis is utilized to study the DC microgrid expandability in terms of integration with multiple DCESs. Finally, several case studies are presented to verify analytical findings of the paper and to evaluate the dynamic performance of the DC microgrid.

The computation speed of computer systems is getting faster and the memory has been enhanced in performance and density through process scaling. However, due to the process scaling, DRAMs are recently suffering from numerous inherent faults. DRAM vendors suggest In-DRAM Error Correcting Code (IECC) to cope with the unreliable operation. However, the conventional IECC schemes have concerns about miscorrection and performance degradation. This paper proposes a pin-aligned In-DRAM ECC architecture using the expandability of a Reed-Solomon code (PAIR), that aligns ECC codewords with DQ pin lines (data passage of DRAM). PAIR is specialized in managing widely distributed inherent faults without the performance degradation, and its correction capability is sufficient to correct burst errors as well. The experimental results analyzed with the latest DRAM model show that the proposed architecture achieves up to 106 times higher reliability than XED with 14% performance improvement, and 10 times higher reliability than DUO with a similar performance, on average.

Nowadays, massive amounts of data are stored in the cloud, how to access control the cloud data has become a prerequisite for protecting the security of cloud data. In order to address the problems of centralized control and privacy protection in current access control, we propose an access control scheme based on the blockchain and re-encryption technology, namely PERBAC-BC scheme. The access control policy is managed by the decentralized and immutability characteristics of blockchain, while the re-encryption is protected by the trusted computing characteristic of blockchain and the privacy is protected by the identity re-encryption technology. The overall structure diagram and detailed execution flow of the scheme are given in this paper. Experimental results show that, compared with the traditional hybrid encryption scheme, the time and space consumption is less when the system is expanded. Then, the time and space performance of each part of the scheme is simulated, and the security of blockchain is proved. The results also show that the time and space performance of the scheme are better and the security is stronger, which has certain stability and expandability.

The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.

The anomaly detection of time series is a hotspot of time series data mining. The own characteristics of different anomaly detectors determine the abnormal data that they are good at. There is no detector can be optimizing in all types of anomalies. Moreover, it still has difficulties in industrial production due to problems such as a single detector can't be optimized at different time windows of the same time series. This paper proposes an adaptive model based on time series characteristics and selecting appropriate detector and run-time parameters for anomaly detection, which is called ATSDLN(Adaptive Time Series Detector Learning Network). We take the time series as the input of the model, and learn the time series representation through FCN. In order to realize the adaptive selection of detectors and run-time parameters according to the input time series, the outputs of FCN are the inputs of two sub-networks: the detector selection network and the run-time parameters selection network. In addition, the way that the variable layer width design of the parameter selection sub-network and the introduction of transfer learning make the model be with more expandability. Through experiments, it is found that ATSDLN can select appropriate anomaly detector and run-time parameters, and have strong expandability, which can quickly transfer. We investigate the performance of ATSDLN in public data sets, our methods outperform other methods in most cases with higher effect and better adaptation. We also show experimental results on public data sets to demonstrate how model structure and transfer learning affect the effectiveness.

In software engineering practice, evaluating and selecting the software testing tools that best fit the project at hand is an important and challenging task. In scientific studies of software engineering, practitioner evaluations and beliefs have recently gained interest, and some studies suggest that practitioners find beliefs of peers more credible than empirical evidence. To study how software practitioners evaluate testing tools, we applied online opinion surveys (n=89). We analyzed the reliability of the opinions utilizing Krippendorff's alpha, intra-class correlation coefficient (ICC), and coefficients of variation (CV). Negative binomial regression was used to evaluate the effect of demographics. We find that opinions towards a specific tool can be conflicting. We show how increasing the number of respondents improves the reliability of the estimates measured with ICC. Our results indicate that on average, opinions from seven experts provide a moderate level of reliability. From demographics, we find that technical seniority leads to more negative evaluations. To improve the understanding, robustness, and impact of the findings, we need to conduct further studies by utilizing diverse sources and complementary methods.

This paper explains the work-in-progress on a vehicle safety and security evaluation platform. Since the testing of cyber-attacks on an actual may be costly or dangerous, the platform enables us to evaluate the threat and the risk associated with cyber-attacks in a safe virtual environment. The goal is to integrate vehicle and powertrain models, mobility and network simulators to actual hardware running the control algorithms using CAN communication. Hardware is selected so as to allows expandability and application of wireless modules which will act as additional attack surfaces. In the current paper, the framework and ideology behind is testbed is described and current progress is shown. A simple GPS spoofing attack on a virtual test vehicle is done and some initial results are discussed.

End-user programmable intelligent agents that can learn new tasks and concepts from users' explicit instructions are desired. This paper presents our progress on expanding the capabilities of such agents in the areas of task applicability, task generalizability, user intent disambiguation and support for IoT devices through our multi-modal approach of combining programming by demonstration (PBD) with learning from natural language instructions. Our future directions include facilitating better script reuse and sharing, and supporting greater user expressiveness in instructions.

We propose a method of eliciting requirements for several different systems together. We focus on systems used by one user at the same time become such systems inherently give influences on with other. We expect such influences help a requirements analyst to be aware of unknown requirements of the user. Any modeling notations are used to explore the combination among systems causing such influences because the differences among the notations give diverse viewpoints to the analyst. To specify such mutual influences, we introduce semantic tags represented by stereo types. We also introduce other semantic tags so that the analyst can judge whether the combination brings advantages to the user. We apply our method to an example and we confirm the method works.

The autonomic logistic system (ALS), first used by the U.S. military JSF, is a new conceptional system which supports prognostic and health management system of aircrafts, including such as real-time failure monitoring, remaining useful life prediction and maintenance decisions-making. However, the development of ALS faces some challenges. Firstly, current ALS is mainly based on client/server architecture, which is very complex in a large-scale aircraft control center and software is required to be reconfigured for every accessed node, which will increase the cost and decrease the expandability of deployment for large scale aircraft control centers. Secondly, interpretation of telemetry parameters from the aircraft is a tough task considering various real-time flight conditions, including instructions from controllers, work statements of single machines or machine groups, and intrinsic physical meaning of telemetry parameters. It is troublesome to meet the expectation of full representing the relationship between faults and tests without a standard model. Finally, typical diagnostic algorithms based on dependency matrix are inefficient, especially the temporal waste when dealing with thousands of test points and fault modes, for the reason that the time complexity will increase exponentially as dependency matrix expansion. Under this situation, this paper proposed a distributed ALS under complex operating conditions, which has the following contributions 1) introducing a distributed system based on browser/server architecture, which is divided overall system into primary control system and diagnostic and health assessment platform; 2) designing a novel interface for modelling the interpretation rules of telemetry parameters and the relationship between faults and tests in consideration of multiple elements of aircraft conditions; 3) proposing a promoted diagnostic algorithm under parallel computing in order to decrease the computing time complexity. what's more, this paper develops a construction with 3D viewer of aircraft for user to locate fault points and presents repairment instructions for maintenance personnels based on Interactive Electronic Technical Manual, which supports both online and offline. A practice in a certain aircraft demonstrated the efficiency of improved diagnostic algorithm and proposed ALS.

New Taipei City is a metropolitan area in Taiwan, with a prosperous economy and a large population. Many parades and public assemblies are held within the jurisdiction and they might have unintended consequences such as traffic, overcrowding, or even serious risks and mishaps. Many cities around the world face similar problems and threats, such as domestic violence in Turkey and the Boston Marathon bombing. Combating new types of public safety threats has become a primary task. Setting up a complete surveillance system at an event site would give a command center access to the site and allow them to respond to an incident in real time. The system's digital records would help track a crime scene, as well as providing insight into how criminals commit crimes. The New Taipei City Police Department has established an "All-Round 3D Technology Security Circle" equipped with various cameras that transmit images back to a command center based on an Internet of Things connection. Innovative technology combined with smart equipment gives three-dimensional coverage of an area to ensure public safety.

The use of green energy is becoming increasingly more important in today's world. Therefore, the use of electric vehicles (EVs) is proving to be the best choice for the environment in terms of public and personal transportation. As the electric vehicles are battery powered, their management becomes very important because using batteries beyond their safe operating area can be dangerous for the entire vehicle and the person onboard. To maintain the safety and reliability of the battery, it is necessary to implement the functionalities of continuous cell monitoring and evaluation, charge control and cell balancing in battery management systems (BMS). This paper presents the development of platform software required for the implementation of these functionalities. This platform is based on a digital signal processing platform which is a master-slave structure. Serial communication technology is adopted between master and slave. This system allows easier controllability and expandability.

During the last years, the Modular Multilevel Matrix Converter (M3C) has been investigated due to its capacity tooperate in high voltage and power levels. This converter is appropriate for Wind Energy Conversion Systems (WECSs), due to its advantages such as redundancy, high power quality, expandability and control flexibility. For Double-Fed Induction Generator (DFIG) WECSs, the M3C has advantages additional benefits, for instance, high power density in the rotor, with a more compact modular converter, and control of bidirectional reactive power flow. Therefore, this paper presents a WECS composed of a DFIG and an M3C. The modelling and control of this WECS topology are described and analyzed in this paper. Additionally, simulation results are presented to validate the effectiveness of this proposal.

This paper proposes a multi-modular AC-DC converter system using wireless communication for a rapid charger of electric vehicles (EVs). The multi-modular topology, which consists of multiple modules, has an advantage on the expandability regarding voltage and power. In the proposed system, the input current and output voltage are controlled by each decentralized controller, which wirelessly communicates to the main controller, on each module. Thus, high-speed communication between the main and modules is not required. As the results in a reduced number of signal lines. The fundamental effectiveness of the proposed system is verified with a 3-kW prototype. In the experimented results, the input current imbalance rate is reduced from 49.4% to 0.1%, where total harmonic distortion is less than 3%.

Series-connected IGBTs, when properly controlled, operate similarly to a single device with a much higher voltage capacity. Integrating series IGBTs into a Modular Multilevel Converter (MMC) can reduce its complexity without compromising the voltage capacity. This paper presents the circuit design on the sub-modular level of a MMC in which all the switching devices are series-connected IGBTs. The voltage sharing among the series IGBTs are regulated in a self-balancing manner. Therefore, no central series IGBT controller is needed, which greatly reduces the sensing and communication complexities, increasing the flexibility and expandability. Hardware experiment results demonstrate that the series IGBTs are able to self-regulate the voltage sharing in a fast and accurate manner and the system can operate similarly to a sub-module in a MMC.

The hybrid microgrid is attracting great attention in recent years as it combines the main advantages of the alternating current (AC) and direct current (DC) microgrids. It is one of the best candidates to support a net-zero energy community. Thus, this paper investigates and compares different hybrid AC/DC microgrid configurations that are suitable for a net-zero energy community. Four different configurations are compared with each other in terms of their impacts on the overall system reliability, expandability, load shedding requirements, power sharing issues, net-zero energy capability, number of the required interface converters, and the requirement of costly medium-voltage components. The results of the investigations indicate that the best results are achieved when each building is enabled to supply its critical loads using an independent AC microgrid that is interfaced to the DC microgrid through a dedicated interface converter.

While the introduction of the softwarelization technologies such as SDN and NFV transfers main focus of network management from hardware to software, the network operators still have to care for a lot of network and computing equipment located in the network center. Toward fully automated network management, we believe that robotic approach will be significant, meaning that robot will care for the physical equipment on behalf of human. This paper explains our experience and insight gained throughout development of a network management robot. We utilize ROS(Robot Operating System) which is a powerful platform for robot development and secures the ease of development and expandability. Our roadmap of the network management robot is also shown as well as three use cases such as environmental monitoring, operator assistance and autonomous maintenance of the equipment. Finally, the paper briefly explains experimental results conducted in a commercial network center.

This article presents a consensus based distributed energy management optimization algorithm for an islanded microgrid. With the rapid development of renewable energy and distributed generation (DG) energy management is becoming more and more distributed. To solve this problem a multi-agent system based distributed solution is designed in this work which uses lambda-iteration method to solve optimization problem. Moreover, the algorithm is fully distributed and transmission losses are also considered in the modeling process which enhanced the practicality of proposed work. Simulations are performed for different cases on 8-bus microgrid to show the effectiveness of algorithm. Moreover, a scalability test is performed at the end to further justify the expandability performance of algorithm for more advanced networks.

Development of information systems dealing with education and labour market using web and grid service architecture enables their modularity, expandability and interoperability. Application of ontologies to the web helps with collecting and selecting the knowledge about a certain field in a generic way, thus enabling different applications to understand, use, reuse and share the knowledge among them. A necessary step before publishing computer-interpretable data on the public web is the implementation of common standards that will ensure the exchange of information. Croatian Qualification Framework (CROQF) is a project of standardization of occupations for the labour market, as well as standardization of sets of qualifications, skills and competences and their mutual relations. This paper analysis a respectable amount of research dealing with application of ontologies to information systems in education during the last decade. The main goal is to compare achieved results according to: 1) phases of development/classifications of education-related ontologies; 2) areas of education and 3) standards and structures of metadata for educational systems. Collected information is used to provide insight into building blocks of CROQF, both the ones well supported by experience and best practices, and the ones that are not, together with guidelines for development of own standards using ontological structures.

Aiming at the realization of power system visualization plane topology modeling, a development method of Microsoft Foundation Classes application framework based on Microsoft Visual Studio is proposed. The overall platform development is mainly composed of five modules: the primitive library module, the platform interface module, the model array file module, the topology array file module, and the algorithm module. The software developed by this method can realize the user-defined power system modeling, and can realize power system operation analysis by combining with algorithm. The proposed method has a short development cycle, compatibility and expandability. This method is applied to the development of a plane topology modeling platform for the distribution network system, which further demonstrates the feasibility of this method.

Bluetooth Low Energy is a fast growing protocol which has gained wide acceptance during last years. Key features for this growth are its high data rate and its ultra low energy consumption, making it the perfect candidate for piconets. However, the lack of expandability without serious impact on its energy consumption profile, prevents its adoption on more complex systems which depend on long network lifetime. Thus, a lot of academic research has been focused on the solution of BLE expandability problem and BLE mesh has been introduced on the latest Bluetooth version. In our point of view, most of the related work cannot be efficiently implemented in networks which are mostly comprised of constrained-resource nodes. Thus, we propose a new energy efficient tree algorithm for BLE static constrained-resources networks, which achieves a longer network lifetime by both reducing as much as possible the number of needed connection events and balancing the energy dissipation in the network.

The Internet of Things (IoT) promises to tackle a range of environmental challenges and deliver large efficiency gains in industry by embedding computational intelligence, sensing and control in our physical environment. Multiple independent parties are increasingly seeking to leverage shared IoT infrastructure, using a similar model to the cloud, and thus require constrained IoT devices to become microservice-hosting platforms that can securely and concurrently execute their code and interoperate. This vision demands that heterogeneous services, peripherals and platforms are provided with an expanded set of security guarantees to prevent third-party services from hijacking the platform, resource-level access control and accounting, and strong isolation between running processes to prevent unauthorized access to third-party services and data. This paper introduces Polyglot CerberOS, a resource-secure operating system for multi-tenant IoT devices that is realised through a reconfigurable virtual machine which can simultaneously execute interoperable services, written in different languages. We evaluate Polyglot CerberOS on IETF Class-1 devices running both Java and C services. The results show that interoperability and strong security guarantees for multilingual services on multi-tenant commodity IoT devices are feasible, in terms of performance and memory overhead, and transparent for developers.

Software1 Defined Network (SDN) is a new type of network architecture that has advantages over traditional network. For SDN, security is a basic issue. SDN controller has received considerable attention in the researches on SDN security. Researchers assign security tasks to SDN controllers generally, but it puts a heavy burden on the SDN controller and leads to a decrease in system stability. On the basis of previous work, we propose a new security protection architecture based on SDN/NFV. Meanwhile, a security function virtual machine pool is designed in infrastructure layer in architecture. It can create virtual machines and manage the ip addresses of the virtual machines automatically, which improves the flexibility and expandability of the architecture. Moreover, a function composition algorithm based on Trie (FCT) has been introduced. We deploy the security protection architecture on VMware cloud platform to validate the availability of the architecture and use experimental data to prove FCT algorithm has good performance.

There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions. We structure our discussion around three of these directions, which we believe are likely to lead to significant progress. The first seeks to design mechanisms for assembling reliable records of compromise that would help understand the degree to which vulnerabilities are exploited by adversaries, as well as favor psychological acceptability of machine learning applications. The second encompasses a spectrum of approaches to input verification and mediation, which is a prerequisite to enable fail-safe defaults in machine learning systems. The third pursues formal frameworks for security and privacy in machine learning, which we argue should strive to align machine learning goals such as generalization with security and privacy desirata like robustness or privacy. Key insights resulting from these three directions pursued both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by systematizing best practices in our growing community.