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Practitioners of multi-objective optimization currently lack open tools that provide decision support through knowledge discovery. There exist many software platforms for multi-objective optimization, but they often fall short of implementing methods for rigorous post-optimality analysis and knowledge discovery from the generated solutions. This paper presents Mimer, a multi-criteria decision support tool for solution exploration, preference elicitation, knowledge discovery, and knowledge visualization. Mimer is openly available as a web-based tool and uses state-of-the-art web-technologies based on WebAssembly to perform heavy computations on the client-side. Its features include multiple linked visualizations and input methods that enable the decision maker to interact with the solutions, knowledge discovery through interactive data mining and graph-based knowledge visualization. It also includes a complete Python programming interface for advanced data manipulation tasks that may be too specific for the graphical interface. Mimer is evaluated through a user study in which the participants are asked to perform representative tasks simulating practical analysis and decision making. The participants also complete a questionnaire about their experience and the features available in Mimer. The survey indicates that participants find Mimer useful for decision support. The participants also offered suggestions for enhancing some features and implementing new features to extend the capabilities of the tool. [ABSTRACT FROM AUTHOR]

Although every educational institution has a code of academic honesty, they still encounter incidents of plagiarism. These are difficult and time-consuming to detect and deal with. This paper explores the use of personalized assessments with the goal of reducing incidents of plagiarism, proposing a personalized assessment software framework through which each student receives a unique problem set. The framework not only auto-generates the problem set but also auto-marks the solutions when submitted. The experience of using this framework is discussed, from the perspective of both students and staff, particularly with respect to its ability to mitigate plagiarism. A comparison of personalized and traditional assignments in the same class confirms that the former had far fewer observed plagiarism incidents. Although personalized assessment may not be cost-effective in all courses (such as language courses), it still can be effective in areas such as mathematics, engineering, science, and computing. This paper concludes that personalized assessment is a promising approach to counter plagiarism. [ABSTRACT FROM PUBLISHER]

The read channel of a Flash memory cell degrades after repetitive program and erase (P/E) operations. This degradation is often modeled as a function of the number of P/E cycles. In contrast, this paper models the degradation as a function of the cumulative effect of the charge written and erased from the cell. Based on this modeling approach, this paper dynamically allocates voltage using lower voltage write thresholds at the beginning of the device lifetime and increasing the thresholds as needed to maintain the mutual information of the read channel in the face of degradation. This paper introduces the technique in an idealized setting and then removes ideal assumptions about channel knowledge and available voltage resolution to conclude with a practical scheme with performance close to that of the idealized setting. [ABSTRACT FROM PUBLISHER]

In supply chain networks of large companies, how to determine the location for establishing distribution centers is an important strategic-level decision problem. This paper proposes a stochastic programming model to determine distribution centers’ locations as well as their scales. The objective of the model is to minimize the expected total transportation cost under uncertain demands of customers. An improved particle swarm optimization algorithm and a Lagrangean relaxation-based solution approach are proposed to solve the model. This paper also performs a case study on applying this model to the largest retailer in China. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods. [ABSTRACT FROM AUTHOR]

Programming by demonstration (PBD) transforms the robot programming from the code level to automated interface between robot and human, promoting the flexibility of robotized automation. In this paper, we focus on programming the industrial robot for assembly tasks by parsing the human demonstration into a series of assembly skills and compiling the skill to the robot executables. To achieve this goal, an identification system using multimodal information to recognize the assembly skill, called MASD, is proposed including: 1) an initial learning stage using a hierarchical model to recognize the action by considering the features from action-object effect, gesture, and trajectory and 2) a retrospective thinking stage using a segmentation method to cut the continuous demonstrations into multiple assembly skills optimally. Using MASD, the demonstration of assembly tasks can be explained with high accuracy in real time, driving a hypothesis that a PBD system on the top of MASD can be extended to more realistic assembly tasks beyond pure positional moving and picking. In experiments, the skill identification module is used to recognize the five kinds of assembly skills in demonstrations of both single and multiple assembly skills, and outperforms the comparative action identification methods. Besides integrated with the MASD, the PBD system can generate the program based on the demonstration and successfully enable an ABB industrial robotic arm simulator to assemble a flashlight and a switch, verifying the initial hypothesis. Note to Practitioners—In the conventional robotized automation, the key role of the robot mainly owes to its capacity for repeating a wide variety of tasks with high speed and accuracy in long term, with a cost of days to months of programming for deployment. On the other hand, the new trend of customization brings the new characteristics: production in short cycle and small volume. This irreversible momentum urges the robot to switch from task to task efficiently. The biggest bottleneck here is the tedious programming, which also has high prerequisites for most practitioners in manufacturing. This situation motivates the development of a PBD system that can understand the assembly skills performed by the human experts in the demonstration and accordingly generate the program for robot’s execution of the taught task. In this paper, we present a skill decoding system to parse the observational raw demonstration into symbolic sequences, which is the crucial bridge to enable the automatic programming. The system achieves high performance in recognition and is tailored for the PBD in assembly tasks by considering both advantages and disadvantages in the background of assembly, such as controllable environment and limited computational resources. It is particularly useful for assembly tasks with modularized actions based on a set of standard parts. At the perspective of industrial application, the PBD upon the proposed system is a promising solution to improve the flexibility of manufacture, which is expected to be true in midterm but an important step toward this goal. [ABSTRACT FROM AUTHOR]

As a comprehensive solution based on the data access frequency, this paper proposes word-line batch $V_{\mathrm {TH}}$ modulation (WBVM) to improve the reliability of 2-D and 3-D-triple-level-cell (TLC) nand flash memories. Proposed WBVM modulates the threshold voltage ($V_{\mathrm {TH}}$) distribution and decreases errors of frequently written/erased “write-hot data,” infrequently written or read “write- and read-cold data,” and frequently read “read-hot data.” For write-hot data, the proposed WBVM $V_{\mathrm {TH}}$ score modulation suppresses the degradation of the tunnel oxide, which is caused by programming higher $V_{\mathrm {TH}}$ state. On the other hand, for write- and read-cold and read-hot data, the proposed WBVM bit error rate (BER) score modulation reduces errors during data retention and read cycles. Moreover, for archive memory, the proposed BER score nLC-modulation achieves the higher reliability by expanding the read margin of the least reliable $V_{\mathrm {TH}}$ state. As a result, the proposed WBVM realizes the comprehensive reliability enhancement for write-hot, write- and read-cold, and read-hot data of 2-D and 3-D-TLC nand flash. WBVM extends the acceptable write/erase cycles, the acceptable data-retention time, and the acceptable read cycles by up to 1.8 $\times$ , 45 $\times$ , and 4.7 $\times$ , respectively. [ABSTRACT FROM AUTHOR]

With the advancement of automation in transportation, the need to improve the operation efficiency of container terminals has increased. The most important determinant of container-handling efficiency is the productivity of equipment, such as quay cranes, automated lifting vehicles, storage yards, and yard cranes. Most previous studies have sought to optimize equipment assignments and scheduling independently and have considered only a loading or an unloading process. As loading and unloading processes occur simultaneously and the equipment operations are highly interrelated, it is important to direct the operations in an integrated manner that reflects the characteristics of automated container terminals. This paper presents a new mixed-integer programming model for analyzing the integrated problem of assigning resources and scheduling, which also considers the limited quantity of critical equipment. To solve the integrated optimization model, a genetic algorithm (GA) is developed. Since the critical equipment, such as yard cranes, are limited, and thus, restricting the efficiency of terminals, a sharing policy is proposed to improve the GA to shorten the operation time of both the loading and unloading processes. Experiments show that the improved GA proposed in this paper can obtain the optimal/near-optimal solutions in short CPU times, therefore it is efficient in solving the integrated equipment assignment and scheduling problem. The results obtained from the sharing policy are superior to those obtained from a non-sharing approach. [ABSTRACT FROM AUTHOR]

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for computational tasks, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth under the quality of service guarantee. Because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether a mobile device stores the data before computing or the data after computing, which tasks to be coded cached and which tasks to be computed locally. The optimization problem is shown to be 0–1 non-convex non-smooth programming, and can be decomposed into a computation offloading programming and a coded caching programming. For a computation offloading subproblem, we proposed an algorithm which applies the convergence of the alternating direction method of multipliers (ADMM) under a non-convex programming and the wide application of the concave-convex procedure (CCCP) for difference of convex (DC) programming to obtain a stationary point, and numerical results verify the convergence of the proposed algorithm and its suboptimality. For the coded cache programming, we design a low complexity algorithm to obtain an acceptable solution. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices. [ABSTRACT FROM AUTHOR]

Maintaining an accurate power system model is crucial for a variety of applications such as electricity markets. In this paper, the cyber security issue induced by critical parameters in electricity market operations is investigated. Critical parameters are a set of model parameters the errors of which cannot be detected due to the lack of local measurements. In this paper, it is first proven that the criticality property of parameters is independent from system operating points, i.e., the maliciously injected errors will always remain undetectable. Then, the cyber attack against critical parameters is formulated as a bilevel programming problem in search of the optimal attack strategy or the worst situation faced by the system operators. An effective algorithm for solving this problem is also provided. Simulation results verify that by injecting undetectable errors into critical model parameters, the locational marginal prices in electricity markets can be significantly distorted, which leads to financial benefits for the attackers. [ABSTRACT FROM AUTHOR]

Hardware non-idealities are among the main performance restrictions for upcoming wireless communication systems. Asymmetric hardware distortions (HWD) happen when the impairments of the I/Q branches are correlated or imbalanced, which in turn generate improper additive interference at the receiver side. When the interference is improper, as well as in other interference-limited scenarios, improper Gaussian signaling (IGS) has been shown to provide rate and/or power efficiency benefits. In this paper, we investigate the rate benefits of IGS in a two-user interference channel (IC) with additive asymmetric HWD when interference is treated as noise. We propose two iterative algorithms to optimize the parameters of the improper transmit signals. We first rewrite the rate region as an pseudo-signal-to-interference-plus-noise-ratio (PSINR) region and employ majorization minimization and fractional programming to find a suboptimal solution for the achievable user rates. Then, we propose a simplified algorithm based on a separate optimization of the powers and complementary variances of the users, which exhibits lower computational complexity. We show that IGS can improve the performance of the two-user IC with additive HWD. Our proposed algorithms outperform proper Gaussian signaling and competing IGS algorithms in the literature that do not consider asymmetric HWD. [ABSTRACT FROM AUTHOR]

Inverter-based Volt/Var control (VVC) methods can effectively address issues of voltage deviations and constraint violations, as well as reduce network power losses in active distribution networks. Conventional VVC mainly relies on rule-based, mathematical, or heuristic methods. However, these methods can be inefficient or even infeasible for large-scale systems, especially when operational uncertainties are considered at both spatial and temporal scales. To this end, firstly, this paper formulates a multi-region coordinated VVC optimization problem to simultaneously minimize bus voltage deviations and network power losses. Furthermore, with the problem being formulated as a partially observable Markov Game, a multi-agent deep deterministic policy gradient (MADDPG) algorithm is modified to solve this problem. Each region, which is a sub-network with a control center, is modeled as an agent and learns to optimize inverter reactive power output setpoints through exploration in virtual environment and improved neural network training procedure. Spatial and temporal uncertainties of photovoltaic power generation and loads are modeled by stochastic programming as scenarios in the MADDPG algorithm. This algorithm can address the uncertainties to guarantee solution robustness. Thus, this paper eventually proposes a robust regionally coordinated VVC method, and numerical simulations indicate high computing efficiency and robust optimum. [ABSTRACT FROM AUTHOR]

In this paper, we propose a vertical transistor with n-bridge and body on gate (BOG-DRAM) for Low-power 1T-DRAM application. The vertical channel of the device can reduce the short-channel effect and improve scalability. The storage region stacked on the gate leads to the efficient utilization of storage space. The device with junctionless channel layers on three sides can improve writing time. The conventional current bridge device only has one side gatecontrol depletion region, but the proposed BOG-DRAM has triple-side gate-control depletion region which can improve programming window at shorter gate lengths. BOG-DRAM achieves programming window of 33.6 μA/μm when the storage length is 20 nm. In addition, the work function offset is exploited for low-power application. [ABSTRACT FROM AUTHOR]

This paper proposes a sequential convex optimization method to solve broader classes of optimal power flow (OPF) problems over radial networks. The non-convex branch power flow equation is decomposed as a second-order cone inequality and a non-convex constraint involving the difference of two convex functions. Provided with an initial solution offered by an inexact second-order cone programming relaxation model, this approach solves a sequence of convexified penalization problems, where concave terms are approximated by linear functions and updated in each iteration. It could recover a feasible power flow solution, which usually appears to be very close, if not equal, to the global optimal one. Two variations of the OPF problem, in which non-cost related objectives are optimized subject to power flow constraints and the convex relaxation is generally inexact, are elaborated in detail. One is the maximum loadability problem, which is formulated as a special OPF problem that seeks the maximal distance to the boundary of power flow insolvability. The proposed method is shown to outperform commercial nonlinear solvers in terms of robustness and efficiency. The other is the bi-objective OPF problem. A non-parametric scalarization model is suggested, and is further reformulated as an extended OPF problem by convexifying the objective function. It provides a single trade-off solution without any subjective preference. The proposed computation framework also helps retrieve the Pareto front of the bi-objective OPF via the $\varepsilon $ -constraint method or the normal boundary intersection method. This paper also discusses extensions for OPF problems over meshed networks based on the semidefinite programming relaxation method. [ABSTRACT FROM PUBLISHER]

The optimization of passenger evacuation path in subway station under emergencies directly affects the evacuation efficiency. Aiming at the evacuation scenario of passengers in subway station under multiple fires, a path planning method based on multi-objective robust optimization is established in this paper. The shortest total evacuation time, the minimum total risk and the minimum total congestion cost are taken as the objectives where the robustness of travel time and risk is also considered. NSGA-II algorithm is used to solve the model, the optimal Pareto solution under a certain robust control parameter is obtained according to the principle of minimizing the total cost function, and the overall optimization degree of route is obtained by using the evaluation index. Taking Qingdao May 4th Square subway station of China as a verification example, a fire diffusion model is built by using Pyrosim, and a passenger evacuation system is built by using Pathfinder. The multi-objective robust path optimization model of passenger evacuation is applied to the built passenger evacuation system under fires. The simulation results show that the overall optimization degree of route can reach 12.8%, when the time and risk robust control parameters are 30 and 30, respectively. The method can be used to guide the passenger evacuation in subway stations under multiple hazard sources and improve the safety index of passenger travel. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-analytical method to obtain the equilibrium bidding strategies for generation and demand units in a combined oligopoly and oligopsony wholesale electricity market. Such market structure is the outcome of the increasing deployment of demand response programs that facilitate active participation of demand-side players in the price-setting process. In this analysis, the concept of supply function equilibrium (SFE) is used to investigate the oligopolistic competition among generation units. The SFE model is extended and the demand function equilibrium (DFE) is obtained to study the oligopsonistic competition among demand units. The economic behavior of a market participant is formulated as a bi-level programming (BLP) problem. The imperfect competition among generation units, as well as among demand units, are modeled as a non-cooperative game. Next, a direct method is developed to calculate all candidate equilibriums of the market, and the locational marginal prices (LMPs) in terms of the bidding strategies of the market participants. The BLP problem is solved by obtaining the coordinated Pareto-dominant Nash equilibrium of the market participants’ non-cooperative games. Finally, the proposed analysis is examined in case studies. Accordingly, we report insightful observations with respect to the impact of the changes in the new market structure, at firm-level and market-level, such as in terms of mitigating market power of generation units, the market clearing prices and quantities, surplus for generation units and demand units, and potential impact on market efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method. [ABSTRACT FROM AUTHOR]

Due to the creation of electricity markets, which are characterized by the competitiveness among participants, particularly in the generation of electric energy, this led to a considerable increase in the number of energy producers. However, in order to maximize their profit, some of them submit their bids beyond competitive prices, incurring in some irregularities in the market. Therefore, this paper analyzes the participants strategy using game theory, because the electricity market is a conflict scenario, in which participants interests are at play. For this purpose, a bilevel optimization model was employed, incorporating a cooperative game theory methodology known as incomplete information gaming, which maximizes the profit of the producers through an optimal strategy and minimizes the systems operating costs. The results obtained in this paper show that, using the game methodology, it is possible to achieve a positive impact on participants profit. However, it is necessary for the participants to be rational and logical when they make their bids. Moreover, the results indicate that this methodology can be used to identify irregularities in the market, such as market manipulation and market power indicators in the participants. [ABSTRACT FROM AUTHOR]

As a challenging attempt to replace a traditional data structure with a learned model, this paper proposes a learned functional Bloom filter (L-FBF) for a key–value storage. The learned model in the proposed L-FBF learns the characteristics and the distribution of given data and classifies each input. It is shown through theoretical analysis that the L-FBF provides a lower search failure rate than a single FBF in the same memory size, while providing the same semantic guarantees. For model training, character-level neural networks are used with pretrained embeddings. In experiments, four types of different character-level neural networks are trained: a single gated recurrent unit (GRU), two GRUs, a single long short-term memory (LSTM), and a single one-dimensional convolutional neural network (1D-CNN). Experimental results prove the validity of theoretical results, and show that the L-FBF reduces the search failures by 82.8% to 83.9% when compared with a single FBF under the same amount of memory used. [ABSTRACT FROM AUTHOR]

Intelligent power regulation is a prominent feature of smart railway systems; to enhance the grid-connected performance of railway loads while economically determining the size of power regulating units, this paper proposes a novel computation-efficient power programming strategy for railway power flow controller (RPFC) to implement power regulation in a.c. railway systems. Two key methods are developed: 1) determining two-phase loads’ PF-P & PF-PF combination model by accurately partitioning two-phase loads’ occurrence region with high probability, and 2) dynamically adjusting the exploring region of two-phase PFs for every load point in 1); in those methods, the intrinsic two-phase PF-PF and PF-P distribution properties of real train loads are fully considered (Note: “P” and “PF” stated above present active power and power factor respectively). In addition, the insight of the compensation principle and the advanced design capacity visualization scheme of RPFC are discussed with emphasis. Finally, a comprehensive performance investigation is carried out to validate the correctness of the proposed method. In this process, an exquisite power regulation unit in digital sense is created, contributing to the system-level application of smart railway systems. [ABSTRACT FROM AUTHOR]

Power systems are shifted from conventional bulk generation toward renewable generation. This trend leads to the frequency security problem due to the decline of system inertia. On the other hand, natural gas-fired units are frequently scheduled to provide operational flexibility due to their fast adjustment ability. The interdependence between the power system and natural gas system is thus intensified. In this context, this paper considers the frequency constrained scheduling problem from the perspective of an integrated electricity-gas system under wind power uncertainty. A distributionally robust (DR) joint chance-constrained optimization model is proposed to co-optimize the unit commitment and virtual inertia provision from wind farm systems. This model incorporates both frequency constraints and natural gas system (NGS) operational constraints and addresses the wind power uncertainty by designing DR joint chance constraints. It is shown that this model admits a mixed-integer second-order cone programming. Case studies demonstrate that the proposed method can provide a highly reliable and computationally efficient solution and show the importance of incorporating NGS operational constraints in the frequency constrained scheduling problem. [ABSTRACT FROM AUTHOR]

Developing efficient strategies for defending electric power systems against attacks is a major concern for contemporary power grids, especially when uncertainties are involved. This paper addresses the allocation of the defensive resource to minimize the damage when there are uncertainties regarding the resource that the attacker has. A multiple-attack-scenario (MAS) defender–attacker–defender (DAD) model is proposed by extending the conventional trilevel DAD model. The proposed model considers the uncertainties related to the offensive resource and the interactions involving the security personnel at the top-level, the attacker at the middle-level, and the power system operator at the bottom-level. The column-and-constraint generation algorithm is implemented by decomposing the MAS DAD model into an upper-level problem for the security personnel, and a lower-level problem for the attacker involving the optimal power flow analysis-based corrective power redispatch implemented by the power system operator. Case studies are performed based on the IEEE RTS79 and 57-bus systems, and the results validate that the proposed method is able to minimize the damage when uncertainties are involved in the offensive resource. This paper can offer meaningful insights into power system protection involving uncertainties. [ABSTRACT FROM AUTHOR]

This paper proposes a new reentry trajectory generation method, which mainly comprises two parts: reentry profile design and trajectory optimization. Different from conventional methods that design angle-of-attack (AOA) and bank-angle profiles, a height-range profile is proposed in this paper. Using this profile, the AOA can be directly derived and the quasi-equilibrium glide condition is easily satisfied. In addition, the bank angle is analytically derived with the heading error, with only one reversal. Finally, the original problem is transformed into a parameter optimization problem and solved by an improved particle swarm optimization (PSO) method. In PSO, a comparison principle is proposed to tackle constraints and three modifications are applied to the swarm evolution to enhance the searching capacity. To conquer disturbances, an online profile updating strategy is developed based on the nominal profile, so complicated work of designing a trajectory tracker is avoided. Simulation validates the effectiveness of the proposed method and demonstrates that the improved PSO is comparable to the pseudospectral method and is better than the basic PSO. [ABSTRACT FROM AUTHOR]

The rank modulation scheme has been proposed for efficient writing and storing data in nonvolatile memory storage. Error correction in the rank modulation scheme is done by considering permutation codes. In this paper, we consider codes in the set of all permutations on n elements, S_{n} , using the Kendall \tau -metric. The main goal of this paper is to derive new bounds on the size of such codes. For this purpose, we also consider perfect codes, diameter perfect codes, and the size of optimal anticodes in the Kendall \tau -metric, structures which have their own considerable interest. We prove that there are no perfect single-error-correcting codes in S_{n} , where n>4 is a prime or 4\leq n\leq 10 . We present lower bounds on the size of optimal anticodes with odd diameter. As a consequence, we obtain a new upper bound on the size of codes in S_{n} with even minimum Kendall \tau -distance. We present larger single-error-correcting codes than the known ones in S5 and S7 . [ABSTRACT FROM AUTHOR]

This paper is concerned with optimal mobile sensor motion coordination for source localization and tracking, taking into account of communication constraints. It is well known that this is a constrained nonlinear optimization problem. The usual way to solve this problem is to adopt a gradient like search algorithm or control strategy to obtain a local optimal solution. In this paper, we propose a novel method, which targets at a global solution for optimal mobile sensor placement in time-difference-of-arrival (TDOA) and hybrid TDOA/angle of arrival-based far-field source localization problems. In the proposed method, first, an analytic solution to optimal mobile sensor positions for source localization and tracking is derived. Then, control laws are designed to force mobile sensors to converge to the expected optimal positions. Simulation results demonstrate our proposed method. [ABSTRACT FROM AUTHOR]

Volt/VAr Optimization (VVO) is becoming increasingly crucial in distribution management systems with renewable sources that require setting their reactive power in coordination with legacy voltage and reactive power control devices. This paper presents a VVO method that operates in complex variables based on the Wirtinger calculus; it employs a penalty method that keeps voltage magnitudes and controller parameters within limits together with a probabilistic rounding technique for handling switched device variables. The complex variable implementation permits using the compensation technique when solving the Karush-Kuhn Tucker (KKT) system, thus achieving a significant speedup due to limiting the number of linear system factorizations. The proposed method is contrasted with a classical VVO implementation employing discrete coordinate search from the current operating point and an enhanced version that uses sensitivity information. Numerical results on distribution networks having up to 3147 nodes show that the proposed method is significantly faster than classical methods and gives operating points free of voltage magnitude violations and lower power loss. [ABSTRACT FROM AUTHOR]

Flying platforms, such as unmanned aerial vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial base stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of self interference cancellation (SIC) techniques in full-duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate; b) maximizing the UL sum-rate; and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one. [ABSTRACT FROM AUTHOR]

Addressing non-convexity plays a fundamental role in solving the optimal electricity-gas flow models. In this paper, an improved spatial branch-and-bound algorithm is proposed to solve the non-convex problem, which is formulated as a mixed-integer bilinear programming, for its exact solution. The core of the algorithm is to divide the non-convex model into convex and small sub-models by branching on specific continuous variables, so that the non-convex problem can be equivalent to a rooted tree for exploration. The exactness of the algorithm is guaranteed by the same criterion as the classical branch-and-bound algorithm. To alleviate the computational burden, a novel two-stage spatial branching strategy is developed to improve the effectiveness and efficiency of the branching operations. The performance of the proposed algorithm is verified on two integrated electricity-gas systems with different sizes. Numerical results demonstrate that our method achieves a balance among feasibility, optimality, and efficiency. The comparison with another 6 convexification-based methods, 3 state-of-the-art non-convex optimization solvers, and 2 spatial branch-and-bound algorithms with classical branching rules further shows the superiority of our algorithm. [ABSTRACT FROM AUTHOR]

The proliferation of distributed energy resources (DERs), located at the Distribution System Operator (DSO) level, bring new opportunities as well as new challenges to the operations within the grid, specifically, when it comes to the interaction with the Transmission System Operator (TSO). To enable interoperability, while ensuring higher flexibility and cost-efficiency, DSOs and the TSO need to be efficiently coordinated. Difficulties behind creating such TSO-DSO coordination include the combinatorial nature of the operational planning problem involved at the transmission level as well as the nonlinearity of AC power flow within both systems. These considerations significantly increase the complexity even under the deterministic setting. In this paper, a deterministic TSO-DSO operational planning coordination problem is considered and a novel decomposition and coordination approach is developed. Within the new method, the problem is decomposed into TSO and DSO subproblems, which are efficiently coordinated by updating Lagrangian multipliers. The nonlinearities at the TSO level caused by AC power flow constraints are resolved through a dynamic linearization while guaranteeing feasibility through “ $l_1-$ proximal” terms. Numerical results based on the coordination of the 118-bus TSO system with up to 32 DSO 34-bus systems indicate that the method efficiently overcomes the computational difficulties of the problem. [ABSTRACT FROM AUTHOR]

Diversity-based security approaches have been studied for several decades since the 1970s. The concept of diversity-by-design emerged in the 1980s. Since then, diversity-based system design research has been explored to provide more secure and dependable services in cyber-physical systems (CPSs). In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, their insights, and associated future work directions for building secure and dependable CPSs. This will allow us to provide promising ways of providing quality network and services based on key diversity-by-design principles for those who want to conduct research on developing secure and dependable CPSs using diversity as a system design feature. This survey paper mainly provides: (i) The common concept of diversity based on its multidisciplinary nature along with the historical evolution of the concept of diversity-by-design for providing secure and dependable services; (ii) the key diversity-by-design principles; (iii) the key benefits and caveats of using the diversity-by-design; (iv) the main concerns of CPS environments utilizing the diversity-by-design; (v) an extensive survey and discussions of existing diversity-based approaches based on five different classifications; (vi) the types of attacks considered by diversity-based approaches; (vii) the overall trends of evaluation methodologies used for diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) the insights, lessons, and gaps identified from this extensive survey and future work directions. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal sizing planning of renewable distributed generation (RDG) in distribution networks to minimize the long-term cost, including the investment cost, maintenance cost, and operating cost. In particular, the operating cost itself is optimized by solving an optimal power flow (OPF) problem at each time $t$ based on uncertain time-varying RDG output and load demand. As a result, the sizing planning problem is a bilevel stochastic programming problem, which is hard to solve. Instead of resorting to conventional meta-heuristic algorithms, this paper first proposes a novel data-driven approach based on the philosophy of online convex optimization to solve the problem with drastically lower complexity. As a key step to facilitate the algorithm, we derive a closed-form expression to iteratively update the sizing solution upon drawing each data sample. With sufficient data samples, the proposed algorithm guarantees to converge to the global optimal solution regardless of the underlying probabilistic distribution of RDG output and load demand. Numerical results on the IEEE 13-bus test feeder, the IEEE 33-bus test feeder, and the Southern California Edison (SCE) 56-bus feeder show that our data-driven method drastically outperforms the other methods in terms of both the solution optimality and computational complexity. [ABSTRACT FROM AUTHOR]

An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed —Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems. [ABSTRACT FROM AUTHOR]

It is hard to obtain precise wind power forecasting due to the chaotic nature of weather systems. Prediction intervals become an efficient tool to quantify the uncertainty involved in wind power forecasting. Traditional central prediction intervals are widely produced by forecasters, however, which might be conservative with respect to interval width and not well fit the practical conditions. This paper develops a novel adaptive bilevel programming (ABP) model, with extreme learning machine based quantile regression as the follower's problem and tuning hyperparameters of quantile proportions as the leader's problem. The proposed ABP model aims at minimizing the average interval width subject to well calibration. In order to overcome the difficulties in disposing of the intractable nested structure of bilevel programming, the ABP model is equivalently transformed into a single-level nonlinear programming problem with bilinear terms. An improved spatial branch-and-bound (ISBB) algorithm is proposed to efficiently solve the reformulated bilinear programming problem. In the ISBB algorithm, an innovative bounds tightening method is developed to tighten the convex relaxation of the bilinear constraint and enhance the convergence. Experimental studies based on actual wind farm of USA under four seasons show the significant effectiveness and high robustness of the developed ABP model, as well as the excellent global optimum attainment and convergence performance of the proposed ISBB algorithm. Moreover, the widely used traditional interval scores are first verified to be prejudiced in probabilistic wind power forecasting evaluation. [ABSTRACT FROM AUTHOR]

This paper presents a floating-gate (FG)-based, field-programmable analog array (FPAA) system-on-chip (SoC) that integrates analog and digital programmable and configurable blocks with a 16-bit open-source MSP430 microprocessor ( \mu \textP ) and resulting interface circuitry. We show the FPAA SoC architecture, experimental results from a range of circuits compiled into this architecture, and system measurements. A compiled analog acoustic command-word classifier on the FPAA SoC requires 23 \mu \textW to experimentally recognize the word dark in a TIMIT database phrase. This paper jointly optimizes high parameter density (number of programmable elements/area/process normalized), as well as high accessibility of the computations due to its data flow handling; the SoC FPAA is 600 $000\times $ higher density than other non-FG approaches. [ABSTRACT FROM AUTHOR]

Multi-objective optimization of power converters is a time-consuming task, especially when multiple operating points and multiple converter topologies must be considered. As a result, various steps are often taken to simplify the design problem and restrict the size of the design space prior to going through an optimization procedure. While this saves time, it produces potentially sub-optimal designs, and existing approaches must tradeoff between running time and design optimality. This paper presents an optimization-oriented method for modeling power converters and their components as posynomial functions, allowing multi-objective optimization of converters to be formulated as a geometric program, a type of convex optimization problem. This allows the use of fast, powerful solvers that guarantee global optimality of solutions. The method is demonstrated using the example of low-power multi-level flying capacitor step-down converters. Results show that, using geometric programming, sets of globally Pareto-optimal designs of two-, three-, and four-level converters with respect to efficiency and power density, for one design space and one operating point, can be generated in as little as 25 s, on a mid- to upper range laptop computer. Thus, optimal designs for three different converter topologies for hundreds of different operating points and/or design spaces can be generated in several hours—less than the time required to globally optimize one converter topology at one operating point for one design space using currently prevalent methods. This paper also demonstrates how geometric programming can be used to quickly perform sensitivity and tradeoff analysis of optimal converter designs. [ABSTRACT FROM AUTHOR]

This paper reviews what changed in the reliability of NAND Flash memory arrays after the paradigm shift in technology evolution determined by the transition from 2-D to 3-D integration schemes. Starting from a quick glance at the fundamentals of raw array reliability, the reasons for its worsening with the evolution of 2-D technologies will be discussed, focusing on the physical phenomena which contributed more to that outcome. By exploring the dependence of the magnitude of these phenomena on cell and array parameters, the abrupt improvements achieved from the 3-D transition in terms of raw array reliability will then be explained, highlighting also that these improvements were turned into new opportunities for the technology. Finally, the physical issues specific to 3-D arrays will be addressed, providing a glimpse of the challenges that the NAND Flash technology will have to face from the standpoint of array reliability in the near future. [ABSTRACT FROM AUTHOR]

This paper proposes a probabilistic optimal power flow (POPF) for unbalanced three-phase electrical distribution systems considering robust constraints. Wind velocity, solar irradiation, and demands are considered as exogenous random variables with dissimilar probability distribution functions. The proposed two-stage POPF model determines the optimal generation dispatch that minimizes the average production cost while satisfying probabilistically robust constraints. The robustness of the solution is adjusted through a parameter involving the first two moments of the random state variables. The $2m+1$ point estimate method (PEM) provides the scenarios for the proposed POPF model, which can be solved using commercial solvers. The model's accuracy is evaluated by comparing the results obtained with the PEM and with a classical scenario generator technique. The model is validated through two IEEE distribution feeders with 13 and 123 nodes, comprising dispatchable and renewable generation. Results show that the model can accurately estimate the mean and standard deviation of the state random variables when compared with results obtained through Monte Carlo simulations. Various levels of conservatism are assessed, demonstrating the model's flexibility and applicability. [ABSTRACT FROM AUTHOR]

Most of the existing caching solutions for wireless networks rest on the ideal assumption that the file popularity distribution is perfectly known. In this paper, we consider optimal random caching designs for perfect, imperfect, and unknown file popularity distributions in a large-scale multi-tier wireless network. First, in the case of perfect file popularity distribution, we formulate the successful transmission probability (STP) optimization problem, which is nonconvex. We propose an efficient parallel iterative algorithm to obtain a stationary point based on parallel successive convex approximation (SCA). Then, in the case of imperfect file popularity distribution, we formulate the worst-case STP maximization problem. To solve this challenging robust optimization problem, we transform it into an equivalent complementary geometric programming (CGP) and propose an efficient iterative algorithm to obtain a stationary point based on the SCA. To the best of our knowledge, this is the first work explicitly considering the estimation error of file popularity distribution in the optimization of caching design. Next, in the case of unknown file popularity distribution, we formulate the stochastic STP (i.e., the STP in the stochastic form) maximization problem. This is a challenging nonconvex stochastic optimization problem, and we propose an efficient iterative algorithm to obtain a stationary point based on stochastic parallel SCA. As far as we know, this is the first work considering stochastic optimization in a large-scale wireless network. Finally, by numerical results, we show that the proposed solutions achieve notable gains over existing schemes in all three cases and reveal the values of the robust caching optimization and stochastic caching optimization in the cases of imperfect file popularity distribution and unknown file popularity distribution, respectively. [ABSTRACT FROM AUTHOR]

To prolong the lifetime of energy constrained devices in Internet of Things, devices can harvest wireless energy from the control signal multicast from the access point. Unfortunately, hampered by the path-loss, the efficiency of such multicast wirelessly powered network is low. While large-scale antennas at access point can be used to improve the efficiency, the beamforming design problem in multicast wirelessly powered network is known to be NP-hard, and the traditional difference of convex programming becomes prohibitively time consuming in large-scale settings. On the other extreme, by using the assumption of infinite number of antennas and applying the law of large numbers, simple beamforming solution is possible. However, when applied to scenarios with finite number of antennas, the performance of such asymptotic solution is far from that of difference of convex programming. To resolve this apparent complexity-performance dilemma, this paper develops an algorithm which reduces the computation time by orders of magnitude, while still guaranteeing the same performance compared with the difference of convex programming. In particular, the proposed algorithm consists of two fast-convergent iterative procedures and is guaranteed to obtain a Karush–Kuhn–Tucker solution. Furthermore, in each iteration, the algorithm only requires the computation of inner products between channel vectors and can be run in parallel for all the users. Thus, the complexity scales linearly with the number of antennas at access point. Finally, numerical results validate the performance and the speed of the proposed scheme. [ABSTRACT FROM AUTHOR]

Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area. [ABSTRACT FROM PUBLISHER]

This paper describes the design and implementation of an 80-kb logic-embedded non-volatile multi-time programmable memory (MTPM) with no added process complexity. Charge trap transistors (CTTs) that exploit charge trapping and de-trapping behavior in high-K dielectric of 32-/22-nm Logic FETs are used as storage elements with logic-compatible programming voltages. A high-gain slew-sense amplifier (SA) is used to efficiently detect the threshold voltage difference ( \Delta V\textrm {DIF} ) between the true and complement FETs in the twin cell. Design-assist techniques including multi-step programming with over-write protection and block write algorithm are used to enhance the programming efficiency without causing a dielectric breakdown. High-temperature stress results show a projected data retention of 10 years at 125 °C with a signal loss of <30% that is margined in while programming, by employing a sense margining logic in the SA. Scalability of CTT has been established by the first demonstration of CTT-based MTPM in 14-nm bulk FinFET technology with read cycle time of 40 ns at 0.7-V VDD. [ABSTRACT FROM AUTHOR]

Signal to interference plus noise ratio (SINR) is an important index for wireless communications. This paper shows a method to derive spreading sequences utilized in code division multiple access systems as the solutions of the non-linear programming: maximize SINR. To this end, we consider a frequency-selective wide-sense-stationary uncorrelated-scattering channel and obtain the worst case of SINR. In the course of the evaluation, we derive an expression of SINR, whose main terms consist of periodic correlation terms and odd periodic correlation terms. Also, we show a relation between SINR and mean square correlations. With our expression, we obtain the problem: maximize SINR. Since this problem is not convex, we obtain the relaxed problem with a semidefinite relaxation technique. From this relaxed problem, we obtain the approximated solutions and evaluate the bit error rate for the solutions. [ABSTRACT FROM AUTHOR]

In this paper, we study a model that mimics the programming operation of memory cells. This model was first introduced by Lastras-Montano et al. for continuous-alphabet channels, and later by Bunte and Lapidoth for discrete memoryless channels (DMC). Under this paradigm we assume that cells are programmed sequentially and individually. The programming process is modeled as transmission over a channel, such that it is possible to read the cell state in order to determine its programming success, and in case of programming failure, to reprogram the cell again. Reprogramming a cell can reduce the bit error rate, however this comes with the price of increasing the overall programming time and thereby affecting the writing speed of the memory. An iterative programming scheme is an algorithm which specifies the number of attempts to program each cell. Given the programming channel and constraints on the average and maximum number of attempts to program a cell, we study programming schemes which maximize the number of bits that can be reliably stored in the memory. We extend the results by Bunte and Lapidoth and study this problem when the programming channel is either discrete-input memoryless symmetric channel (including the BSC,BEC, BI-AWGN) or the $Z$ channel. For the BSC and the BEC our analysis is also extended for the case where the error probabilities on consecutive writes are not necessarily the same. Lastly, we also study a related model which is motivated by the synthesis process of DNA molecules. [ABSTRACT FROM AUTHOR]

Charts go hand in hand with text to communicate complex data and are widely adopted in news articles, online blogs, and academic papers. They provide graphical summaries of the data, while text explains the message and context. However, synthesizing information across text and charts is difficult; it requires readers to frequently shift their attention. We investigated ways to support the tight coupling of text and charts in data documents. To understand their interplay, we analyzed the design space of chart-text references through news articles and scientific papers. Informed by the analysis, we developed a mixed-initiative interface enabling users to construct interactive references between text and charts. It leverages natural language processing to automatically suggest references as well as allows users to manually construct other references effortlessly. A user study complemented with algorithmic evaluation of the system suggests that the interface provides an effective way to compose interactive data documents. [ABSTRACT FROM AUTHOR]

Thanks to the strong ability against the inter-cell interference, cell-free network is considered as a promising technique to improve network capacity. However, further capacity improvement requires to deploy more base stations (BSs) with high cost and power consumption. To address this issue, inspired by the recently developed reconfigurable intelligent surface (RIS) technique, we propose the concept of RIS-aided cell-free network to improve the capacity with low cost and power consumption. The key idea is to replace some of the required BSs by low-cost and energy-efficient RISs. Then, in a wideband RIS-aided cell-free network, we formulate the problem of joint precoding design at BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization framework to solve this challenging problem. In particular, we decouple this problem via fractional programming, and solve the subproblems alternatively. Note that most of the scenarios considered in existing works are special cases of the general scenario studied in this paper, and the proposed joint precoding framework can serve as a general solution to maximize the capacity in most existing RIS-aided scenarios. Finally, simulation results demonstrate that, compared with the conventional cell-free network, the network capacity under the proposed scheme can be improved significantly. [ABSTRACT FROM AUTHOR]