The performance of general-purpose colour CRT monitors in PACS environment

General-purpose colour cathode ray tube (CRT) monitors are used commonly for image display in personal computer-based picture archiving and communication system (PACS) and telemedicine systems. At present, however, we have not enough information about their performance or reliability for this task. Therefore, we studied the performance of five general-purpose colour CRTs and the changes in their performance in a year. Resolution was measured visually using original digital images. Maximum and minimum luminances were measured at the centre and periphery of the CRTs. Distortion was measured in the centre and periphery using original digital images. All the monitors met their specifications. The inhomogeneity of luminance exceeded 20% and varied across the CRTs. Darker monitors took more time to reach stable luminance levels. The corner brightness seemed to be a good estimator of both performance and distortion in colour monitors.     

Decision capabilities of diagnostic experiments with linear automata

In solving diagnostic problems, the use of simple conditional and multiple unconditional experiments is demonstrated to give no radically new information as compared with simple unconditional experiments. Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 62–65, May–June, 2000.     

Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

We describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

     

Extending the enforcement power of truncation monitors using static analysis

Runtime monitors are a widely used approach to enforcing security policies. Truncation monitors are based on the idea of truncating an execution before a violation occurs. Thus, the range of security policies they can enforce is limited to safety properties. The use of an a priori static analysis of the target program is a possible way of extending the range of monitorable properties. This paper presents an approach to producing an in-lined truncation monitor, which draws upon the above intuition. Based on an a priori knowledge of the program behavior, this approach allows, in some cases, to enforce more than safety properties and is more powerful than a classical truncation mechanism. We provide and prove a theorem stating that a truncation enforcement mechanism considering only the set of possible executions of a specific program is strictly more powerful than a mechanism considering all the executions over an alphabet of actions.     

Issues on representational capabilities of artificial neural networks and their implementation

This work is an organized review on the representational capabilities of artificial neural networks and the questions that arise in their implementation. It covers the Kolmogorov's superposition theorem and different statements regarding how it could be related to the representational power of neural networks. Generalization capability of neural networks is then considered and methods of improving this capability are discussed. Some theorems and statements concerning the bound on the number of hidden layers, form of the activation function, and time complexity of training of neural networks are other subjects of this article. © 1995 John Wiley & Sons, Inc.     

A method of vertical and horizontal force estimation by using air-filled material and camera for soft physical human-robot interaction: fundamental experiments

A method of vertical and horizontal force estimation for soft physical human–robot interaction by using an air-filled material and a camera was proposed in this study. First, we considered hypotheses for vertical and horizontal force applied to the air-filled material. Second, the prototype of air-filled material and vision-based force-sensing device (AVFSD) was constructed. Based on the result of preliminary experiments, the relationships between force, pneumatic pressure, and contact surface displacement were formulated. The vertical and horizontal force could be estimated using the AVFSD with percentage errors of 3.6 and 5.8%, respectively, and the hypotheses were verified through the experiments.     

Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence

Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics’ evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies - artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process-based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey.     

Prospects for process selection using artificial intelligence

On problem facing modern industry is the lack of a skilled labor force to produce machined parts as has been done in the past. In the near future, this problem may become acute for a number of manufacturing tasks. One such task is process planning. Since process planning requires intelligent reasoning and considerable experiential knowledge, almost all existing computer aided process planning systems require a significant amount of supervision by an experienced human being.There is some prospect that “expert computer system” techniques from the field of Artificial Intelligence may be successfully used to automate (at least partially) several of the reasoning activities involved with process planning. This paper discusses some current prospects for automating a process planning task known as process selection. These ideas are currently being considered for use int he Automated Manufacturing Research Facility project at the U.S. National Bureau of Standards, and steps are being taken to implement them in an expert computer system.     

Emergence of adaptive behaviors for artificial creature using a combined artificial neural network

This study proposes a method to acquire adaptive behavior for artificial creature which has a lot of joints using a combined Artificial Neural Network (ANN). Experiment in this study focuses on artificial fish model, which has a lot of joints, tracking towards a target in the virtual water environment. In order to control motions of joints, a combined ANN is implemented with the model. At first, one ANN is prepared to control specific joints so as to swim basically in response to minimal input information using evolutionary computation in preliminary experiments. And an new network is constructed by combining its network and the other network. In order to acquire complicate behavior for artificial creature, weights of combined ANN are optimized. Experiment result shows the model which has many joints acquire adaptive swimming behavior towards a target by optimizing combined network.     

Taming access control security: extending capabilities using the views relationship

The ‘views’ relationship indicates how an object‐oriented design can be clearly separated into objects and their corresponding interface. This paper uses the concept of ‘views’ in order to achieve full separation between the application and the security policy in the design and implementation. The result is achieved by providing a model for capabilities using ‘views’ that is richer than the traditional capability model. In addition, a distributed access control model is shown to be effective through the use of Secure Object Communication Channels (SOCCs) to allow for secure connections at the abstract object level. This security is applicable in the e‐commerce application domain, bringing security directly to the application abstraction level. Copyright © 2002 John Wiley & Sons, Ltd.     

Efficient design of experiments for structural optimization using significance screening

When performing structural optimization of large scale engineering problems, the choice of experiment design is important. However, classical experiment designs are developed to deal with undesired but inevitable scatter and are thus not ideal for sampling of deterministic computational responses. In this paper, a novel screening and design of computer experiments algorithm is presented. It is based on the concept of orthogonal design variable significances and is applicable for problems where design variables do not simultaneously have a significant influence on any of the constraints. The algorithm presented uses significance orthogonality to combine several one-factor-at-a-time experiments in one several-factors-at-a-time experiment. The procedure results in a reduced experiment design matrix. In the reduced experiment design, each variable is varied exactly once but several variables may be varied simultaneously, if their significances with respect to the constraints are orthogonal. Moreover, a measure of influence, as well as an influence significance threshold, is defined. In applications, the value of the threshold is left up to the engineer. To assist in this choice, a relation between model simplification, expressed in terms of the significance threshold, and computational cost is established in a screening. The relation between efficiency and loss of accuracy for the proposed approach is discussed and demonstrated. For two solid mechanics type problems studied herein, the necessary number of simulations could be reduced by 25% and 64%, respectively, with negligible losses in accuracy.     

用分子模拟方法预测MOFs材料氢吸附的能力

用分子力学力场模拟法预测材料的储氢性能,为实验合成提供理论指导,降低开发成本和时间,有重要的实用意义.本文采用第一性原理推导的分子力学力场及巨正则系综蒙特卡洛模拟(GCMC)法和Feyman-Hibbs有效势计算氢分子在MOF-5体系的吸附曲线,所得不同温度和压力下的超额及绝对吸附曲线均与实验吻合良好,表明模拟法和力场的准确性.于是,预测了一系列不同MOFs材料的储放氢性能后,探讨材料结构和储氢能力.发现化学结构相似的MOFs材料,影响材料储放氢能力的关键因素是材料的自由体积.     

Sound direction estimation using an artificial ear for robots

We propose a novel design of an artificial robot ear for sound direction estimation using two measured outputs only. The spectral features in the interaural transfer functions (ITFs) of the proposed artificial ears are distinctive and move monotonically according to the sound direction. Thus, these features provide effective sound cues to estimate sound direction using the measured two output signals. Bilateral asymmetry of microphone positions can enhance the estimation performance even in the median plane where interaural differences vanish. We propose a localization method to estimate the lateral and vertical angles simultaneously. The lateral angle is estimated using interaural time difference and Woodworth and Schlosberg’s formula, and the front–back discrimination is achieved by finding the spectral features in the ITF estimated from two measured outputs. The vertical angle of a sound source in the frontal region is estimated by comparing the spectral features in the estimated ITF with those in the database built in an anechoic chamber. The feasibility of the designed artificial ear and the estimation method were verified in a real environment. In the experiment, it was shown that both the front–back discrimination and the sound direction estimation in the frontal region can be achieved with reasonable accuracy. Thus, we expect that robots with the proposed artificial ear can estimate the direction of speaker from two output signals only.     

计算机CRT设备特征描述文件的建立

按照国际颜色协会ICC制定的开放式色彩管理标准，该文采用BP网络算法模型，建立CRT的设备特征描述Profile文件，实现基于ICC标准的CRT色彩管理。     

Revisiting tests for neglected nonlinearity using artificial neural networks

Tests for regression neglected nonlinearity based on artificial neural networks (ANNs) have so far been studied by separately analyzing the two ways in which the null of regression linearity can hold. This implies that the asymptotic behavior of general ANN-based tests for neglected nonlinearity is still an open question. Here we analyze a convenient ANN-based quasi-likelihood ratio statistic for testing neglected nonlinearity, paying careful attention to both components of the null. We derive the asymptotic null distribution under each component separately and analyze their interaction. Somewhat remarkably, it turns out that the previously known asymptotic null distribution for the type 1 case still applies, but under somewhat stronger conditions than previously recognized. We present Monte Carlo experiments corroborating our theoretical results and showing that standard methods can yield misleading inference when our new, stronger regularity conditions are violated.     

Behavioral task processing for cognitive robots using artificial emotions

This paper presents an artificial emotional-cognitive system-based autonomous robot control architecture for a four-wheel driven and four-wheel steered mobile robot. Discrete stochastic state-space mathematical model is considered for behavioral and emotional transition processes of the autonomous mobile robot in the dynamic realistic environment. The term of cognitive mechanism system which is composed from rule base and reinforcement self-learning algorithm explain all of the deliberative events such as learning, reasoning and memory (rule spaces) of the autonomous mobile robot. The artificial cognitive model of autonomous robot control architecture has a dynamic associative memory including behavioral transition rules which are able to be learned for achieving multi-objective robot tasks. Motivation module of architecture has been considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors for long-term action planning. Also reinforcement self-learning and reasoning ability of artificial cognitive model and motivational gain effects of proposed architecture can be observed on the executing behavioral sequences during simulation. The posture and speed of the robot and the configurations, speeds and torques of the wheels and all deliberative and cognitive events can be observed from the simulation plant and virtual reality viewer. This study constitutes basis for the multi-goal robot tasks and artificial emotions and cognitive mechanism-based behavior generation experiments on a real mobile robot.     

Rethinking the design of virtual machine monitors

A virtual machine monitor is a software system that partitions a single physical machine into multiple virtual machines. Traditionally, VMMs have created a precise replica of the underlying physical machine. Through faithful emulation, VMMs support the execution of legacy guest operating systems such as Windows or Linux without modifications. However, traditional VMMs suffer from poor scalability and extensibility. To overcome the poor scalability and extensibility of traditional virtual machine monitors that partition a single physical machine into multiple virtual machines, the Denali VMM uses paravirtualization to promote scalability and hardware interposition to promote extensibility.     

Analyzing the capabilities of crowdsourcing services for text summarization

This paper presents a detailed analysis of the use of crowdsourcing services for the Text Summarization task in the context of the tourist domain. In particular, our aim is to retrieve relevant information about a place or an object pictured in an image in order to provide a short summary which will be of great help for a tourist. For tackling this task, we proposed a broad set of experiments using crowdsourcing services that could be useful as a reference for others who want to rely also on crowdsourcing. From the analysis carried out through our experimental setup and the results obtained, we can conclude that although crowdsourcing services were not good to simply gather gold-standard summaries (i.e., from the results obtained for experiments 1, 2 and 4), the encouraging results obtained in the third and sixth experiments motivate us to strongly believe that they can be successfully employed for finding some patterns of behaviour humans have when generating summaries, and for validating and checking other tasks. Furthermore, this analysis serves as a guideline for the types of experiments that might or might not work when using crowdsourcing in the context of text summarization.     

Positioning strategic competitiveness of green business innovation capabilities using hybrid method

There are intensive studies on the issues of environment practice and business innovation capability, have been dealt with extensively from practitioners and academicians, however, studies on the evaluation of green business innovation capabilities (GBICs) are few. This study tackles the evaluation assessment using fuzzy set theory, analytical network process and importance–performance analysis. Specifically, this study is using the theoretical framework of GBICs and proposed hybrid approach, which are new in the theoretical and empirical literatures. An expert group was empirical assessment to the perception on PWB sector and a manufacturing firm is evaluated. The analytical results indicated that this proposed hybrid method is a suitable and effective method for identifying and analyzing the strategic competitiveness of GBICs at case firm, especially when evaluation criteria are dependent in uncertainty.