Class-based Method Research Articles

Hazardous Chemical Accident Prevention Based on K-Means Clustering Analysis of Incident Information

Hazardous chemicals are inflammable, explosive, and/or toxic and are prone to accidental leakage, fire, and explosion during production, storage, and transportation. It is time-consuming and laborious to study the properties of hazardous chemicals individually for systematic accident prevention because of the wide variety of hazardous chemicals and conditions resulting in accidents. Moreover, accidents have numerous causes, and the relationships among the causative factors are complex. It is a problem that is difficult to accurately identify the effects of correlations among accident factors and determine the laws governing accident occurrence. In this paper, we propose a generic method of hazardous chemical accident prevention based on K-means clustering analysis of incident information to illustrate how to solve the problems. A database of hazardous chemical incidents was constructed, and a K-means clustering algorithm was adopted to classify the incidents. The numbers of occurrences and frequencies of the words in the textual descriptions of the consequences, processes, and causes of hazardous chemical incidents were counted and calculated using a class-based method. For words with a high frequency, risk scenarios were constructed, checklist items of newly revealed dangers were developed, and a system for systematic risk assessment and accident prevention was established. Finally, the information on hazardous material transportation incidents in the Pipeline and Hazardous Materials Safety Administration database of the U.S. Department of Transportation from 2009 to 2018 had been taken as an example to illustrate the method application. The results demonstrate that the proposed method of hazardous chemical accident prevention can be used to improve accident classification. The classification results make it possible to determine the optimal sequence of key targets on which to focus and the requirements for accident prevention and formulate preventive measures. Thus, they provide a technical basis for accident prevention.

Estimating base cation weathering rates in the United States: challenges of uncertain soil mineralogy and specific surface area with applications of the PROFILE model.

The weathering release rate of base cations (BCw) from soil minerals is fundamentally important for terrestrial ecosystem growth, function, and sensitivity to acid deposition. Understanding BCw is necessary to reduce or prevent damage to acid-sensitive natural systems, in that this information is needed to both evaluate the effectiveness of existing policies, and guide establishment of further policies in the event they are required. Yet BCw is challenging to estimate. In this study, major sources of uncertainty associated with a process-based model (PROFILE) commonly used to estimate weathering rates were quantified in the context of efforts to quantify BCw for upland forest sites across the continental U.S. These include uncertainty associated with parameterization of mineral content where horizon data are not available, stoichiometry of individual minerals, and specific surface area of soil and individual soil minerals. Mineral stoichiometry was not an important influence on BCw estimates (uncertainty < 1%). Characterizing B horizon mineralogy by averaging A and C horizons was found to be a minor (< 5%) contributor to uncertainty in some areas, but where mineralogy is known to vary with depth the uncertainty can be large. Estimating mineral-specific surface areas had a strong influence on estimated BCw, with rates increasing by as much as 250%. The greatest uncertainty in BCw estimates, however, was attributed to the particle size class-based method used to estimate the total specific surface area upon which weathering reactions can take place. The resulting uncertainty in BCw spanned multiple orders of magnitude at individual sites, highlighting this as the greatest challenge to ongoing efforts to produce robust BCw estimates across large spatial scales in the U.S. Recommendations for improving estimates of BCw to support robust decision making for protection against terrestrial acidification are provided.

Reflexivity in Teaching Responsible Management Outside of the Classroom

This paper discusses how the design of service-learning projects can foster students’ reflexivity in learning responsible management. The paper builds on the existing debate on the nature of reflexivity. It proposes to focus on the relationship between students and the structure of responsible management teaching as defined by the curriculum, the learning outcomes, and the expectations of Business Schools. The paper adopts Archer’s morphogenetic conceptual approach to explore analytically this agency-structure relationship in service-learning projects. Drawing on parallels with ancient Greek theatre, the paper investigates how this relationship can morph via praxis and dialogue and affect reflexivity. The paper reflects on the empirical evidence from two service-learning projects. Each was run twice: once using a traditional class-based method and once using the Aristotelian approach to Greek Theatre. The two versions considered different configurations of the dimensions of time, space and action as well as of the role of the teacher in the student’s reflexive process. Empirical evidence highlights how students are more likely to take control of their own learning by enacting praxis in service-learning projects that are compressed in time, space, and course of action. Moreover, the reflexive journey changes when the teacher acts as a dialogical interlocutor as opposed to be a mere instructor in the project. The paper introduces implications for Business Schools in terms of teachers’ training in preparation for responsible management teaching. It also discusses the design for effective service-learning projects and collaboration with external agencies.

Applying object-based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification

In general, considerable human and material resources are required for performing a forest inventory survey. Using remote sensing technologies to save forest inventory costs has thus become an important topic in forest inventory-related studies. Leica ADS-40 digital aerial photographs feature advantages such as high spatial resolution, high radiometric resolution, and a wealth of spectral information. As a result, they have been widely used to perform forest inventories. We classified ADS-40 digital aerial photographs according to the complex forest land cover types listed in the Fourth Forest Resource Survey in an effort to establish a classification method for categorizing ADS-40 digital aerial photographs. Subsequently, we classified the images using the knowledge-based classification method in combination with object-based analysis techniques, decision tree classification techniques, classification parameters such as object texture, shape, and spectral characteristics, a class-based classification method, and geographic information system mapping information. Finally, the results were compared with manually interpreted aerial photographs. Images were classified using a hierarchical classification method comprised of four classification levels (levels 1 to 4). The classification overall accuracy (OA) of levels 1 to 4 is within a range of 64.29% to 98.50%. The final result comparisons showed that the proposed classification method achieved an OA of 78.20% and a kappa coefficient of 0.7597. On the basis of the image classification results, classification errors occurred mostly in images of sunlit crowns because the image values for individual trees varied. Such a variance was caused by the crown structure and the incident angle of the sun. These errors lowered image classification accuracy and warrant further studies. This study corroborates the high feasibility for mapping complex forest land cover types using ADS-40 digital aerial photographs.

Vari-gram language model based on word clustering

Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks: 1) The problem of word clustering. It is hard to find a suitable clustering method with good performance and less computation. 2) Class-based method always loses the prediction ability to adapt the text in different domains. In order to solve above problems, a definition of word similarity by utilizing mutual information was presented. Based on word similarity, the definition of word set similarity was given. Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance, and the perplexity is reduced from 283 to 218. At the same time, an absolute weighted difference method was presented and was used to construct vari-gram language model which has good prediction ability. The perplexity of vari-gram model is reduced from 234.65 to 219.14 on Chinese corpora, and is reduced from 195.56 to 184.25 on English corpora compared with category-based model.

Vari-Gram Language Model Based on Category

Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks about this model: (1) the problem of word clustering, it is hard to find a suitable clustering method that has good performance and not large amount of computation. (2) class based method always lose some prediction ability to adapt the text of different domain. The authors try to solve above problems in this paper. This paper presents a novel definition of word similarity. Based on word similarity, this paper gives the definition of word set similarity. Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance. At the same time, this paper presents a new method to create the vari-gram model.

A Computation Control Motion Estimation Method for Complexity-Scalable Video Coding

In this paper, a new computation-control motion estimation (CCME) method is proposed which can perform motion estimation (ME) adaptively under different computation or power budgets while keeping high coding performance. We first propose a new class-based method to measure the macroblock (MB) importance where MBs are classified into different classes and their importance is measured by combining their class information as well as their initial matching cost information. Based on the new MB importance measure, a complete CCME framework is then proposed to allocate computation for ME. The proposed method performs ME in a one-pass flow. Experimental results demonstrate that the proposed method can allocate computation more accurately than previous methods and, thus, has better performance under the same computation budget.

Dynamic availability-oriented control of the automated storage/retrieval system. A computer integrated manufacturing perspective

Dynamic scheduling of the storage/retrieval (S/R) requests and increasing the utilization of the storage spaces, are the two common approaches to improve the efficiency of the automated storage/retrieval systems (AS/RS) in industry practice. However, the actual improvement of the AS/RS has to be justified from the perspective of computer integrated manufacturing system (CIMS) because the ultimate objective of the AS/RS is to assist the CIMS in enhancing its material handling efficiency as well as synchronizing the production. Targeting on this crucial requirement, a dynamic availability-oriented controller (DAOC) is developed in this paper to minimize the mean request fulfilment time of the AS/RS. Firstly, while considering the three types of critical dynamic constraints from the CIMS’ shop floor: the availabilities for both machines and the AS/RS, the process plan of the product, and the production schedule, DAOC is capable of determining the optimal execution sequence of the S/R requests issued from the CIMS and activating dynamic storage space consolidation commands of the AS/RS. Secondly, simulation experiments are conducted and the results show that DAOC successfully improves the S/R request control scheme of the traditional class-based assignment method and significantly outperforms four comparable approaches under various CIMS configurations.

Statistical language modeling based on variable-length sequences

In natural language and especially in spontaneous speech, people often group words in order to constitute phrases which become usual expressions. This is due to phonological (to make the pronunciation easier), or to semantic reasons (to remember more easily a phrase by assigning a meaning to a block of words). Classical language models do not adequately take into account such phrases. A better approach consists in modeling some word sequences as if they were individual dictionary elements. Sequences are considered as additional entries of the vocabulary, on which language models are computed. In this paper, we present a method for automatically retrieving the most relevant phrases from a corpus of written sentences. The originality of our approach resides in the fact that the extracted phrases are obtained from a linguistically tagged corpus. Therefore, the obtained phrases are linguistically viable. To measure the contribution of classes in retrieving phrases, we have implemented the same algorithm without using classes. The class-based method outperformed by 11% the other method. Our approach uses information theoretic criteria which insure a high statistical consistency and make the decision of selecting a potential sequence optimal in accordance with the language perplexity. We propose several variants of language model with and without word sequences. Among them, we present a model in which the trigger pairs are linguistically more significant. We show that the use of sequences decrease the word error rate and improve the normalized perplexity. For instance, the best sequence model improves the perplexity by 16%, and the the accuracy of our dictation system (MAUD) by approximately 14%. Experiments, in terms of perplexity and recognition rate, have been carried out on a vocabulary of 20,000 words extracted from a corpus of 43 million words made up of two years of the French newspaper Le Monde. The acoustic model (HMM) is trained with the Bref80 corpus.

A Method for Estimating Strength of Association and Its Application to Structural Disambiguation

This paper proposes a new class-based method for estimating the strength of association for word co-occurrences. To deal with sparseness of data we first use a conceptual dictionary to find upper classes of the words in the co-occurrence relation. We then use t-scores to determine a pair of classes to be used in the estimation. The strength of association for the word co-occurrence is calculated using the classes thus obtained. We have applied our method to determining dependency relations in Japanese and prepositional phrase attachment in English. The experimental results show that our method is sound, effective and useful in resolving different types of ambiguities.

Joseph Hersey Pratt’s Cost-Effective Class Method and Its Contemporary Application: Some Problems in Biopsychosocial Innovation

Problems reflecting the stress of illness and the illness of stress account for 20-50% of medical visits and an unquantified but substantial portion of the billions of dollars spent annually on tests and procedures (Stoeckle et al. 1964). The "high-tech" form of practice, favored so powerfully by American medicine and supported so lavishly by financial incentives, produces limited benefits in this domain of suffering and medical care. In an era of increasing panic about the runaway cost of health care, medicine desperately needs cost-effective forms of "low-tech" treatment for patients with psychosomatic conditions and the emotional dimensions of chronic and recurrent illness. Between 1905 and 1955 Joseph Hersey Pratt (1872-1956), an eminent Boston internist and professor of medicine, developed what appears to have been a highly efficient, class-based method for treating such patients in an empathic and biotechnically sound manner. Pratt pioneered his technique in the Emmanuel Church Tuberculosis Class between 1905 and 1923, the work for which he is best known. Seven years later he adapted the method for treatment of patients with chronic psychosomatic conditions, and from 1930 to 1955 he again appears to have achieved considerable success in a highly efficient manner in what was called the Thought Control Class at the Boston Dispensary. Although the treatment of tuberculosis has of course been totally transformed by antibiotics, psychosomatic conditions and the emotional dimensions of chronic illnesses like tuberculosis form an ever larger component of the suffering seen in general medical practices. For this reason, a historical study of Pratt's techniques, theories, and the fate of his innovations may provide practical guidance for contemporary health care practice.