New Solution Approaches to the General Single- Machine Earliness-Tardiness Problem

This paper addresses the general single-machine earliness-tardiness problem with distinct release dates, due dates, and unit costs. The aim of this research is to obtain an exact nonpreemptive solution in which machine idle time is allowed. In a hybrid approach, we formulate and then solve the problem using dynamic programming (DP), while incorporating techniques from branch-and-bound (BB). This approach (DP-BB) has been proven to be effective in solving certain types of scheduling problems. We further propose a new adaptation of the approach to a general problem with a nonregular objective function. To address some shortcomings of DP-BB, we also apply a BB approach in which partial dynamic programming dominance (BB-PDP) is exploited. Computational experiments were conducted with randomly generated test instances in order to evaluate the effectiveness of the two approaches. The results clearly showed that our new approaches can solve all the instances with up to 40 jobs and most of the instances with 50 jobs, which outperforms those frequently used approaches in scheduling research.     

Hot-carrier degradation of submicrometer p-MOSFETs withthermal/LPCVD composite oxide

It is shown that while gate oxides containing thermal/LPCVD composite oxide have lower defect densities than gates using only thermal oxides, they are more susceptible to hot-carrier degradation. The hot-carrier-induced degradation of composite oxides is worse in p-channel MOSFETs than in n-channel MOSFETs. This sensitivity of p-channel MOSFETs is caused by higher electron trapping levels in LPCVD oxides. For 150-Å gate technology, the hot-carrier-degradation resistance of thermal/LPCVD composite gate oxides with a 70-Å or thicker thermal oxide layer approaches that of high-quality pure thermal oxide     

Real-time moustache detection by combining image decolorization and texture detection with applications to facial gender recognition

There are still many challenging problems in facial gender recognition which is mainly due to the complex variances of face appearance. Although there has been tremendous research effort to develop robust gender recognition over the past decade, none has explicitly exploited the domain knowledge of the difference in appearance between male and female. Moustache contributes substantially to the facial appearance difference between male and female and could be a good feature to be incorporated into facial gender recognition. Little work on moustache segmentation has been reported in the literature. In this paper, a novel real-time moustache detection method is proposed which combines face feature extraction, image decolorization and texture detection. Image decolorization, which converts a color image to grayscale, aims to enhance the color contrast while preserving the grayscale. On the other hand, moustache appearance is normally grayscale surrounded by the skin color face tissue. Hence, it is a fast and efficient way to segment the moustache by using the decolorization technology. In order to make the algorithm robust to the variances of illumination and head pose, an adaptive decolorization segmentation has been proposed in which both the segmentation threshold selection and the moustache region following are guided by some special regions defined by their geometric relationship with the salient facial features. Furthermore, a texture-based moustache classifier is developed to compensate the decolorization-based segmentation which could detect the darker skin or shadow around the mouth caused by the small lines or skin thicker from where he/she smiles as moustache. The face is verified as the face containing a moustache only when it satisfies: (1) a larger moustache region can be found by applying the decolorization segmentation; (2) the segmented moustache region is detected as moustache by the texture moustache detector. The experimental results on color FERET database showed that the proposed approach can achieve 89 % moustache face detection rate with 0.1 % false acceptance rate. By incorporating the moustache detector into a facial gender recognition system, the gender recognition accuracy on a large database has been improved from 91 to 93.5 %.     

Optimal process conditions of shrinkage and warpage of thin‐wall parts

Optimal process conditions of thin‐wall injection molding of a cellular phone cover were investigated with the consideration of interaction effects between process parameters. L₂₇ experimental tests based on Taguchi's method were performed, and then Cyclone Scanner, PolyCAD and PolyWorks were used to measure the shrinkage and warpage of the thin‐wall injected parts to determine the optimal process conditions. Based on the results of the analysis of variables and the F‐test, interaction effects for each observed factor were determined. The results indicated that the packing pressure was the most important process parameter affecting the shrinkage and warpage of the thin‐wall part. The optimal process conditions were different for the shrinkage and the warpage. This was because during the injection process, the mechanisms affecting shrinkage or warpage were different. Compared with the results obtained with simplified thin‐wall parts in the literature, it was found that the geometry of a real commercial part did affect the optimal process conditions and the order of influence of process parameters. The optimal process conditions determined by Taguchi's method for reducing the shrinkage and warpage were verified experimentally in this work. Polym. Eng. Sci. 44:917–928, 2004. © 2004 Society of Plastics Engineers.     

Scheduling for electricity cost in a smart grid

We study an offline scheduling problem arising in demand response management in a smart grid. Consumers send in power requests with a flexible set of timeslots during which their requests can be served. For example, a consumer may request the dishwasher to operate for 1 h during the periods 8am to 11am or 2pm to 4pm. The grid controller, upon receiving power requests, schedules each request within the specified duration. The electricity cost is measured by a convex function of the load in each timeslot. The objective of the problem is to schedule all requests with the minimum total electricity cost. As a first attempt, we consider a special case in which the power requirement and the duration a for which a request needs service are both unit-size. For this problem, we present a polynomial time offline algorithm that gives an optimal solution and shows that the time complexity can be further improved if the given set of timeslots forms a contiguous interval.     

Cryptanalysis of simple three-party key exchange protocol (S-3PAKE)

Password-authenticated key exchange (PAKE) protocols allow parties to share secret keys in an authentic manner based on an easily memorizable password. Recently, Lu and Cao proposed a three-party password-authenticated key exchange protocol, so called S-3PAKE, based on ideas of the Abdalla and Pointcheval two-party SPAKE extended to three parties. S-3PAKE can be seen to have a structure alternative to that of another three-party PAKE protocol (3PAKE) by Abdalla and Pointcheval. Furthermore, a simple improvement to S-3PAKE was proposed very recently by Chung and Ku to resist the kind of attacks that applied to earlier versions of 3PAKE. In this paper, we show that S-3PAKE falls to unknown key-share attacks by any other client, and undetectable online dictionary attacks by any adversary. The latter attack equally applies to the recently improved S-3PAKE. Indeed, the provable security approach should be taken when designing PAKEs; and furthermore our results highlight that extra cautions still be exercised when defining models and constructing proofs in this direction.     

Development of an integrated look-ahead dynamics-based NURBS interpolator for high precision machinery

Methodologies for planning motion trajectory of parametric interpolation such as non-uniform rational B-spline (NURBS) curves have been proposed in the past. However, most of the algorithms were developed based on the constraints of feedrate, acceleration/deceleration (acc/dec), jerk, and chord errors. The errors caused by servo dynamics were rarely included in the design process. This paper proposes an integrated look-ahead dynamics-based (ILD) algorithm which considers geometric and servo errors simultaneously. The ILD consists of three different modules: a sharp corner detection module, a jerk-limited module, and a dynamics module. The sharp corner detection module identifies sharp corners of a curve and then divides the curve into small segments. The jerk-limited module plans the feedrate profile of each segment according to the constraints of feedrate, acc/dec, jerk, and chord errors. To ensure that the contour errors are bounded within the specified value, the dynamics module further modifies the feedrate profile based on the derived contour error equation. Simulations and experiments are performed to validate the ILD algorithm. It is shown that the ILD approach improves tracking and contour accuracies significantly compared to adaptive-feedrate and curvature-feedrate algorithms.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Attribute-Based Signature with Policy-and-Endorsement Mechanism

In this paper a new signature scheme, called Policy-Endorsing Attribute-Based Signature, is developed to correspond with the existing Ciphertext-Policy Attribute-Based Encryption. This signature provides a policy-and-endorsement mechanism. In this mechanism a single user, whose attributes satisfy the predicate, endorses the message. This signature allows the signer to announce his endorsement using an access policy without having to reveal the identity of the signer. The security of this signature, selfless anonymity and existential unforgeability, is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear map groups.     

An SNS-based model for finding collaborative partners

This paper proposes a model, Recommendation of Appropriate Partners (RAP), used on a Social Networking Service (SNS) for locating appropriate “helpers” for users based on individual users’ Chain of Friends (CoF) relationships. Using the RAP model, individual users can participate in a collaborative online community in remote locations, whereby helpers are willing to help other users solve their tasks/problems, and it is intended that both the users and helpers gain knowledge from these interactive online sessions. An example of the RAP-based system was implemented to invite Program Committee members to an international conference. The system was evaluated and the experimental results show that our model is very effective for discovering collaboration partners and finding users with similar interests in order to create communities for providing future and longer-term helping exchange.